Systems, devices and methods for non-invasive image-based plaque analysis and risk assessment - Patents.com

Abstract

A system and method for facilitating a determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis. The method may include accessing a non-invasively generated medical image, identifying one or more arteries, identifying an area of ​​plaque within the arteries, analyzing the area of ​​plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque at least in part on the basis of density, determining a distance from the identified area of ​​low density non-calcified plaque to one or more of a lumen wall or a vessel wall, determining a degree of embedment of the area of ​​low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the greater area of ​​low density non-calcified plaque, and generating a display of the analysis to facilitate one or more determinations of a subject's risk of CAD.

Description

[Technical Field]

[0001] [Priority and Related Applications] This application claims the benefit of U.S. Provisional Application No. 63 / 269,136, filed March 10, 2022, U.S. Provisional Application No. 63 / 362,108, filed March 29, 2022, U.S. Provisional Application No. 63 / 362,856, filed April 12, 2022, U.S. Provisional Application No. 63 / 364,078, filed May 3, 2022, U.S. Provisional Application No. 63 / 365,381, filed May 3, 2022, and U.S. Provisional Application No. 63 / 368,381, filed May 26, 2022. No. 63 / 364,084 (filed May 3, 2022), U.S. Provisional Application No. 63 / 365,381 (filed May 26, 2022), U.S. Provisional Application No. 63 / 368,293 (filed July 13, 2022), and U.S. Provisional Application No. 63 / 381,210 (filed October 27, 2022), each of which is incorporated by reference in its entirety herein.

[0002] This application is related to U.S. Patent No. 10,813,612, filed January 23, 2020, U.S. Patent No. 11,501,436, filed January 5, 2021, and U.S. Patent No. 11,302,001, filed August 4, 2021, and U.S. Application No. 17 / 820,439, filed August 17, 2022, each of which is incorporated herein by reference in its entirety.

[0003] This application relates to non-invasive image-based plaque analysis and risk assessment. Summary of the Invention

[0004] Various embodiments described herein relate to systems, devices, and methods for non-invasive, image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein relate to analysis of one or more regions of plaque, such as coronary artery plaque, based on one or more distance, volume, shape, morphology, depth of implantation, and / or axis measurements. For example, in some embodiments, the systems, devices, and methods described herein relate to plaque analysis based on one or more of the following: the distance between the plaque and the vessel wall, the distance between the plaque and the lumen wall, the length along the anterior-posterior axis, the length along the lateral axis, the volume of low-density non-calcified plaque, the volume of total plaque, the ratio between the volume of low-density non-calcified plaque and the volume of total plaque, the depth of implantation of low-density non-calcified plaque, and / or the like. In some embodiments, the systems, devices, and methods described herein are configured to determine the risk of coronary artery disease (CAD), such as myocardial infarction (MI), based on one or more plaque analyses described herein. In some embodiments, the systems, devices, and methods described herein are configured to generate suggested treatments and / or visual representations based on the determined CAD risk and / or one or more plaque analyses described herein.

[0005] In some embodiments, systems, devices, and methods relate to facilitating the determination of risk for coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis. In some embodiments, the systems, devices, and methods include accessing non-invasively acquired medical images of a subject; analyzing the medical images of the subject to identify one or more arteries; identifying one or more regions of plaque in one or more coronary arteries; analyzing the identified one or more regions of plaque to identify one or more regions of low-density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density; and analyzing one or more regions of low-density non-calcified plaque in response to identifying the one or more regions of low-density non-calcified plaque, wherein Analyzing the one or more regions of low-density, non-calcified plaque includes determining a distance from the one or more regions of low-density, non-calcified plaque to one or more of the lumen wall or vascular wall, determining the degree of embedding of the one or more regions of low-density, non-calcified plaque in the one or more regions of non-calcified or calcified plaque, and determining a shape of the one or more regions of low-density, non-calcified plaque; and generating a display of the analysis of the one or more regions of low-density, non-calcified plaque based at least in part on the results of the analysis of the one or more regions of low-density, non-calcified plaque, which facilitates determining the subject's risk of CAD.

[0006] In some embodiments, a determination that the distance from one or more regions of low-density non-calcified plaque to the lumen wall is less than a predetermined threshold is indicative of vulnerable plaque or an elevated risk of CAD. In some embodiments, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined on a three-dimensional basis. In some embodiments, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined based on the two-dimensional image. In some embodiments, two-dimensional images are obtained by taking two-dimensional slices perpendicular to the anterior-posterior (longitudinal) axis of a normalized multiplanar view of one or more arteries. In some embodiments, the two-dimensional image is obtained by capturing a two-dimensional slice that maximizes the two-dimensional area of ​​low-density non-calcified plaque. In some embodiments, the distance from one or more regions of low-density non-calcified plaque to the lumen wall is determined by determining the shortest distance between the boundary of one or more regions of low-density non-calcified plaque and the boundary of the lumen wall. In some embodiments, the distance from one or more regions of low-density non-calcified plaque to the vessel wall is determined by determining the shortest distance between the boundary of one or more regions of low-density non-calcified plaque and the boundary of the vessel wall. In some embodiments, the one or more arteries comprise one or more coronary or carotid arteries. In some embodiments, the one or more axes of the one or more regions of low-density non-calcified plaque comprise one or more of a long axis in a longitudinal section, a short axis in a longitudinal section, a long axis in a transverse section, or a short axis in a transverse section. In some embodiments, one or more axes are determined on a three-dimensional basis. In some embodiments, the one or more axes are determined based on one or more two-dimensional images. In some embodiments, longitudinal sections are obtained by taking two-dimensional slices parallel to the anterior-posterior (longitudinal) axis of a normalized multiplanar view of one or more arteries. In some embodiments, the longitudinal section is obtained by taking a two-dimensional slice that provides the longest longitudinal axis of the longitudinal section. In some embodiments, the degree of coverage of the one or more regions of low-density non-calcified plaque is determined based at least in part on visually superimposing a protractor over the one or more regions of low-density non-calcified plaque on a medical image. In some embodiments, a high degree of embedding of one or more regions of low-density non-calcified plaque indicates a high risk of vulnerable plaque or CAD. In some embodiments, the shape of one or more regions of low-density non-calcified plaque is determined as one or more of crescent, round, lobe-shaped, or bean-shaped. In some embodiments, determining one or more regions of low-density non-calcified plaque as round or bean-shaped indicates vulnerable plaque or a high risk of CAD. In some embodiments, the shape of one or more regions of low-density non-calcified plaque is determined based at least in part on a machine learning algorithm. In some embodiments, analyzing the one or more regions of low-density non-calcified plaque further comprises determining the length of one or more axes of the one or more regions of low-density non-calcified plaque. In some embodiments, the shape of one or more regions of low-density non-calcified plaque is determined based at least in part on one or more determined lengths of one or more axes. In some embodiments, the shape of one or more regions of low-density non-calcified plaque is determined based at least in part on determining a standard deviation of one or more determined lengths of the one or more axes. In some embodiments, analyzing the one or more regions of low-density non-calcified plaque further includes determining a volume of the one or more regions of low-density non-calcified plaque, determining a volume of the one or more regions of plaque, and determining a ratio of the volume of the one or more regions of low-density non-calcified plaque to the volume of the one or more regions of plaque. In some embodiments, the volume of one or more regions of low-density non-calcified plaque is determined based on determining one or more of the lengths of one or more axes of said one or more regions of low-density non-calcified plaque. In some embodiments, a determination that the volume of one or more regions of low-density non-calcified plaque is equal to or greater than a predetermined threshold is indicative of vulnerable plaque or a high risk of CAD. In some embodiments, a determination that the volume of one or more regions of plaque is equal to or greater than a predetermined threshold is indicative of unstable plaque or a high risk of CAD. In some embodiments, a determination that the ratio of the volume of one or more regions of low-density non-calcified plaque to the volume of one or more regions of plaque is equal to or greater than a predetermined threshold is indicative of unstable plaque or a high risk of CAD. In some embodiments, the density comprises an absolute density. In some embodiments, the density comprises a radial density. In some embodiments, the medical image is obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, magnetic resonance imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS).

[0007] Other embodiments disclosed herein relate to systems, devices, and methods related to assessing cardiovascular risk and / or disease status using image-based analysis of vascular surface and / or feature coordinates. In some embodiments, cardiovascular risk and / or disease status assessments generated using the systems, methods, and devices herein can be utilized in generating patient diagnoses and / or suggested treatments.

[0008] Other embodiments disclosed herein relate to systems, devices, and methods related to assessing cardiovascular risk and / or disease and / or condition using image-based analysis of one or more regions and / or features of non-calcified and / or calcified plaque. In some embodiments, the assessment of cardiovascular risk and / or disease and / or condition generated using the systems, methods, and devices herein may be utilized in the diagnosis of a patient and / or the generation of a suggested treatment.

[0009] Other embodiments disclosed herein relate to systems, devices, and methods related to assessing cardiovascular risk and / or disease and / or condition using corrected and / or normalized image analysis-based plaque parameters. In some embodiments, the cardiovascular risk and / or disease and / or condition assessments generated using the systems, methods, and devices herein may be utilized in generating a patient diagnosis and / or suggested treatment.

[0010] Other embodiments disclosed herein relate to systems, devices, and methods for generating patient-specific reports regarding risk and / or status assessment, diagnosis, and / or treatment of cardiovascular disease, including, for example, coronary artery disease (CAD). In particular, in some embodiments, the systems, devices, and methods are configured to generate immersive, patient-specific reports regarding a patient's cardiovascular disease risk, status, diagnosis, and / or treatment. In some embodiments, the systems, devices, and methods are configured to generate immersive, patient-specific reports based, at least in part, on image-based analysis of, for example, one or more plaque and / or vascular parameters.

[0011] Other embodiments disclosed herein relate to assessing cardiovascular risk and / or disease and / or condition using plaque parameters based on normalized image analysis. In some embodiments, the assessment of cardiovascular risk and / or disease and / or condition generated using the systems, methods, and devices herein can be utilized in the diagnosis of a patient and / or the generation of a suggested treatment.

[0012] Other embodiments disclosed herein relate to systems, devices, and methods for non-invasive imaging determination of fractional flow reserve (FFR) and / or ischemia. In particular, in some embodiments, the systems, devices, and methods relate to FFR and / or ischemia analysis of arteries, such as coronary arteries, aorta, and / or carotid arteries, using one or more image analysis techniques. For example, in some embodiments, the systems, methods, and devices can be configured to derive one or more stenosis and / or normal measurements from non-invasively obtainable medical images and use the same to derive an FFR and / or ischemia assessment. In some embodiments, the systems, methods, and devices can be configured to apply one or more allometric scaling laws to the one or more stenosis and / or normal measurements to derive and / or generate an FFR and / or ischemia assessment.

[0013] In some embodiments, a computer-implemented method for facilitating a determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis includes: accessing, by a computer system, non-invasively acquired medical images of a subject; analyzing, by the computer system, the medical images of the subject to identify one or more arteries; identifying, by the computer system, one or more regions of plaque in the one or more coronary arteries; analyzing, by the computer system, the identified one or more regions of plaque to identify, at least in part, one or more regions of low-density non-calcified plaque, non-calcified plaque, or calcified plaque; and, in response to identifying the one or more regions of low-density non-calcified plaque, analyzing one or more regions of low-density non-calcified plaque, where analyzing the one or more regions of low-density non-calcified plaque includes determining a distance from the one or more regions of low-density non-calcified plaque to one or more of the lumen wall or the vascular wall, determining a degree of embedding of the one or more regions of low-density non-calcified plaque in the one or more regions of non-calcified plaque or calcified plaque, and determining a shape of the one or more regions of low-density non-calcified plaque; and generating, by the computer system, a display of the analysis of the one or more regions of low-density non-calcified plaque based at least in part on the results of the analysis of the one or more regions of low-density non-calcified plaque, the display facilitating determination of the subject's risk of CAD, wherein the computer system comprises a computer processor and an electronic storage medium.

[0014] In some embodiments of the computer-implemented method, a determination that the distance from one or more regions of low-density non-calcified plaque to the lumen wall is less than a predetermined threshold indicates a high risk of vulnerable plaque or CAD. In some embodiments of the computer-implemented method, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined on a three-dimensional basis. In some embodiments of the computer-implemented method, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined based on a two-dimensional image. In some embodiments of the computer-implemented method, the two-dimensional image is obtained by capturing two-dimensional slices perpendicular to the longitudinal axis (anterior-posterior axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the computer-implemented method, the two-dimensional image is obtained by capturing a two-dimensional slice in which the two-dimensional area of ​​the low-density non-calcified plaque is maximized. In some embodiments of the computer-implemented method, the distance from one or more regions of low-density non-calcified plaque to the lumen wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the lumen wall. In some embodiments of the computer-implemented method, the distance from one or more regions of low-density non-calcified plaque to the vessel wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the vessel wall. In some embodiments of the computer-implemented method, the one or more arteries comprise one or more coronary arteries or carotid arteries.

[0015] In some embodiments of the computer-implemented method, the one or more axes of the one or more regions of low-density non-calcified plaque comprise one or more of a long axis in a longitudinal section, a short axis in a longitudinal section, a long axis in a transverse section, or a short axis in a transverse section. In some embodiments of the computer-implemented method, the one or more axes are determined on a three-dimensional basis. In some embodiments of the computer-implemented method, the one or more axes are determined based on one or more two-dimensional images. In some embodiments of the computer-implemented method, the longitudinal section is obtained by taking a two-dimensional slice parallel to the longitudinal axis (anterior-posterior axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the computer-implemented method, the longitudinal section is obtained by taking a two-dimensional slice that provides the longest long axis of the longitudinal section. In some embodiments of the computer-implemented method, the transverse section is obtained by taking a two-dimensional slice perpendicular to the longitudinal axis (anterior-posterior axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the computer-implemented method, the transverse section is obtained by taking a two-dimensional slice perpendicular to the long axis on the longitudinal section. In some embodiments of the computer-implemented method, the cross-section is obtained by taking a two-dimensional slice that provides the largest two-dimensional area of ​​low-density non-calcified plaque. In some embodiments of the computer-implemented method, one or more of the analyses of the one or more areas of low-density non-calcified plaque are performed by a computer system.

[0016] In some embodiments of the computer-implemented method, the degree of coverage of one or more regions of low-density non-calcified plaque is determined based at least in part on visually overlaying a protractor over one or more regions of low-density non-calcified plaque on a medical image. In some embodiments of the computer-implemented method, a high degree of coverage of one or more regions of low-density non-calcified plaque indicates a high risk of unstable plaque or CAD. In some embodiments of the computer-implemented method, the shape of one or more regions of low-density non-calcified plaque is determined as one or more of crescent, round, leaf-like, or bean-shaped. In some embodiments of the computer-implemented method, determining one or more regions of low-density non-calcified plaque as round or bean-shaped indicates a high risk of unstable plaque or CAD. In some embodiments of the computer-implemented method, the shape of one or more regions of low-density non-calcified plaque is determined based at least in part on a machine learning algorithm.

[0017] In some embodiments of the computer-implemented method, analyzing one or more regions of low-density non-calcified plaque further comprises determining the lengths of one or more axes of the one or more regions of low-density non-calcified plaque. In some embodiments of the computer-implemented method, the shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on the determined lengths of one or more axes. In some embodiments of the computer-implemented method, the shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on determining a standard deviation of the determined lengths of one or more of the one or more axes. In some embodiments of the computer-implemented method, analyzing one or more regions of low-density non-calcified plaque further comprises determining the volume of the one or more regions of low-density non-calcified plaque, determining the volume of the one or more regions of plaque, and determining a ratio of the volume of the one or more regions of low-density non-calcified plaque to the volume of the one or more regions of plaque. In some embodiments of the computer-implemented method, the volume of one or more regions of low-density non-calcified plaque is determined based on determining one or more axial lengths of one or more regions of low-density non-calcified plaque. In some embodiments of the computer-implemented method, a determination that the volume of one or more regions of low-density non-calcified plaque is equal to or greater than a predetermined threshold indicates a high risk of unstable plaque or CAD. In some embodiments of the computer-implemented method, a determination that the ratio of the volume of one or more regions of low-density non-calcified plaque to the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates a high risk of unstable plaque or CAD.

[0018] In some embodiments of the computer-implemented method, the density comprises absolute density. In some embodiments of the computer-implemented method, the density comprises radiometric density. In some embodiments of the computer-implemented method, one or more regions of plaque are identified as low-density non-calcified plaque if the radiometric density value is between about -189 and about 30 Hounsfield units. In some embodiments of the computer-implemented method, one or more regions of plaque are identified as calcified plaque if the radiometric intensity value is between about 351 and about 2500 Hounsfield units. In some embodiments of the computer-implemented method, the medical image comprises a computed tomography (CT) image. In some embodiments of the computer-implemented method, the medical image is obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, MR imaging, magnetic resonance imaging (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS). In some embodiments of the computer-implemented method, the method further includes generating, by the computer system, an assessment of the subject's risk of CAD or the risk of one or more regions of plaque based at least in part on the analysis of the one or more regions of low-density non-calcified plaque. In some embodiments of the computer-implemented method, the method further includes generating, by the computer system, a recommended treatment for the subject based at least in part on the analysis of the one or more regions of low-density non-calcified plaque.

[0019] In some embodiments, a system for facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis comprises one or more computer-readable storage devices configured to store a plurality of computer-executable instructions, and one or more hardware computer processors in communication with the one or more computer-readable storage devices and configured to execute the plurality of computer-executable instructions to cause the system to: access non-invasively acquired medical images of a subject; analyze the medical images of the subject to identify one or more arteries; identify one or more regions of plaque in the one or more coronary arteries; analyze the identified one or more regions of plaque to identify one or more regions of low-density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density; and analyze one or more regions of low-density non-calcified plaque in response to identifying one or more regions of low-density non-calcified plaque, wherein one of said low-density non-calcified plaque Analyzing the regions includes determining a distance from one or more regions of low-density, non-calcified plaque to one or more of the lumen wall or vascular wall, determining a degree of embedding of the one or more regions of low-density, non-calcified plaque in the one or more regions of non-calcified plaque or calcified plaque, and determining a shape of the one or more regions of low-density, non-calcified plaque; and generating a display of the analysis of the one or more regions of low-density, non-calcified plaque based at least in part on the analysis of the one or more regions of low-density, non-calcified plaque that facilitates determining the subject's risk of CAD.

[0020] In some embodiments of the system, a determination that the distance from one or more regions of low-density non-calcified plaque to the lumen wall is less than a predetermined threshold indicates unstable plaque or a high risk of CAD. In some embodiments of the system, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined on a three-dimensional basis. In some embodiments of the system, the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined based on a two-dimensional image. In some embodiments of the system, the two-dimensional image is obtained by taking two-dimensional slices perpendicular to the anterior-posterior axis (longitudinal axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the system, the two-dimensional image is obtained by taking a two-dimensional slice that results in the largest two-dimensional area of ​​low-density non-calcified plaque. In some embodiments of the system, the distance from one or more regions of low-density non-calcified plaque to the lumen wall is determined by determining the shortest distance between the boundary of one or more regions of low-density non-calcified plaque and the boundary of the lumen wall. In some embodiments of the system, the distance from the one or more regions of low-density non-calcified plaque to the vessel wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the vessel wall. In some embodiments of the system, the one or more arteries comprise one or more coronary arteries or carotid arteries.

[0021] In some embodiments of the system, the one or more axes of the one or more regions of low-density non-calcified plaque include one or more of a long axis on a longitudinal section, a short axis on a longitudinal section, a long axis on a transverse section, or a short axis on a transverse section. In some embodiments of the system, the one or more axes are determined on a three-dimensional basis. In some embodiments of the system, the one or more axes are determined based on one or more two-dimensional images. In some embodiments of the system, the longitudinal section is obtained by taking a two-dimensional slice parallel to the anterior-posterior axis (longitudinal axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the system, the longitudinal section is obtained by taking a two-dimensional slice that provides the longest long axis of the longitudinal section. In some embodiments of the system, the transverse section is obtained by taking a two-dimensional slice perpendicular to the anterior-posterior axis (longitudinal axis) of a normalized multiplanar view of one or more arteries. In some embodiments of the system, the transverse section is obtained by taking a two-dimensional slice perpendicular to the long axis on the longitudinal section. In some embodiments of the system, the transverse section is obtained by taking a two-dimensional slice that provides the largest two-dimensional area of ​​low-density non-calcified plaque. In some embodiments of the system, one or more of the analyses of the one or more regions of low-density non-calcified plaque are performed by a computer system.

[0022] In some embodiments of the system, the degree of coverage of one or more regions of low-density non-calcified plaque is determined based at least in part on visually overlaying a protractor on one or more regions of low-density non-calcified plaque on a medical image. In some embodiments of the system, a high degree of coverage of one or more regions of low-density non-calcified plaque indicates a high risk of unstable plaque or CAD. In some embodiments of the system, the shape of one or more regions of low-density non-calcified plaque is determined as one or more of crescent, round, leaf, or bean shape. In some embodiments of the system, determining one or more regions of low-density non-calcified plaque as round or bean shape indicates a high risk of unstable plaque or CAD. In some embodiments of the system, the shape of one or more regions of low-density non-calcified plaque is determined based at least in part on a machine learning algorithm.

[0023] In some embodiments of the system, analyzing one or more regions of low-density non-calcified plaque further includes determining one or more lengths of one or more axes of the one or more regions of low-density non-calcified plaque. In some embodiments of the system, a shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on the determined one or more lengths of the one or more axes. In some embodiments of the system, a shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on determining a standard deviation of the determined one or more lengths of the one or more axes. In some embodiments of the system, analyzing one or more regions of low-density non-calcified plaque further includes determining a volume of the one or more regions of low-density non-calcified plaque, determining a volume of the one or more regions of plaque, and determining a ratio of the volume of the one or more regions of low-density non-calcified plaque to the volume of the one or more regions of plaque. In some embodiments of the system, the volume of one or more regions of low-density non-calcified plaque is determined based on determining one or more axial lengths of the one or more regions of low-density non-calcified plaque. In some embodiments of the system, a determination that the volume of one or more regions of low-density non-calcified plaque is equal to or greater than a predetermined threshold indicates a high risk of unstable plaque or CAD. In some embodiments of the system, a determination that the ratio of the volume of one or more regions of low-density non-calcified plaque to the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates a high risk of unstable plaque or CAD.

[0024] In some embodiments of the system, the density comprises absolute density. In some embodiments of the system, the density comprises radiometric density. In some embodiments of the system, one or more regions of the plaque are identified as low-density non-calcified plaque when the radiometric density value is between about -189 and about 30 Hounsfield units. In some embodiments of the system, one or more regions of the plaque are identified as non-calcified plaque when the radiometric density value is between about 31 and about 350 Hounsfield units. In some embodiments of the system, one or more regions of the plaque are identified as calcified plaque when the radiometric intensity value is between about 351 and about 2500 Hounsfield units. In some embodiments of the system, the medical image comprises a computed tomography (CT) image. In some embodiments of the system, the medical image is obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS). In some embodiments of the system, the system is further adapted to generate an assessment of the subject's risk of CAD or the risk of one or more regions of plaque based at least in part on the analysis of the one or more regions of low-density non-calcified plaque. In some embodiments of the system, the system is further adapted to generate a recommended treatment for the subject based at least in part on the analysis of the one or more regions of low-density non-calcified plaque.

[0025] For purposes of this Summary, certain aspects, advantages, and novel features of the invention are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular embodiment of the invention. Thus, for example, one skilled in the art will recognize that the invention can be embodied or practiced in a way that achieves one advantage or group of advantages as taught herein, without necessarily achieving other advantages that may be taught or suggested herein.

[0026] All of these embodiments are intended to be within the scope of the invention disclosed herein. These and other embodiments will be readily apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying figures, but the invention is not limited to the specific embodiment(s) disclosed. [Brief explanation of the drawings]

[0027] A better understanding of the apparatus and methods described herein will be understood by reference to the following description in conjunction with the accompanying drawings:

[0028] [Figure 1] 1 shows a schematic diagram of an example embodiment of a system 100 including a processing system configured to characterize coronary artery plaque.

[0029] [Figure 2] 1 is a schematic diagram showing an example of the myocardium and its coronary arteries.

[0030] [Figure 3] FIG. 1 shows an example series of images generated from a scan along a coronary artery, including selected images of a portion of the coronary artery, and how the image data correspond to values ​​on the Hounsfield scale.

[0031] [Figure 4A] FIG. 1 is a block diagram illustrating a computer system in which various embodiments may be implemented.

[0032] [Figure 4B] 4 is a block diagram illustrating computer modules of a computer system 400 in which various embodiments may be implemented.

[0033] [Figure 5A] 1 shows an example of a flowchart of a process for analyzing coronary artery plaque.

[0034] [Figure 5B]5B shows an example of a flowchart that expands a portion of the flowchart of FIG. 5A for determining characteristics of coronary artery plaque.

[0035] [Figure 6] 1 is a representation of image data showing an example of a portion of a coronary artery (sometimes referred to herein as a "vessel" for ease of reference).

[0036] [Figure 7] Illustrates the same blood vessel and plaque and fat characteristics as illustrated in FIG. 6, and further illustrates additional examples of areas of arteries, and plaque and / or perivascular fat in the vicinity of arteries, that may be analyzed to determine characteristics of a patient's arteries.

[0037] [Figure 8A] FIG. 1 is a block diagram illustrating an example of a process by which features in a medical image are identified using artificial intelligence or machine learning.

[0038] [Figure 8B] FIG. 1 is a schematic diagram illustrating an example of a neural network for determining patient characteristics based on medical images.

[0039] [Figure 8C] 1 illustrates a flowchart for training an artificial intelligence or machine learning model, according to some embodiments.

[0040] [Figure 8D] 1 illustrates an example of training and using an AI / ML model according to some embodiments.

[0041] [Figure 9A]9A-9E illustrate examples of sample measurements, dimensions, and / or analyses related to plaque analysis and risk assessment. Figure 9A(a) illustrates a low-density non-calcified plaque (LDNC) aggregate associated with a single coronary artery lesion; Figure 9A(b) illustrates the distance to the lumen and vessel wall; Figure 9A(c) illustrates the dimension axes along the major and minor axes; Figure 9A(d) illustrates the depth of embedding (DELP) of the LDNC plaque; and Figure 9A(e) illustrates four different shapes of plaque or plaque regions, including crescent, round, leaf, and bean shapes.

[0042] [Figure 9B] Examples of high-risk plaque morphology characteristics by lesion obtained from sample studies are shown.

[0043] [Figure 9C] Examples of atherosclerotic characteristics per patient from a sample study stratified by the presence or absence of acute coronary syndrome events are shown.

[0044] [Figure 9D] Examples of adjusted hazard ratios for the effect of high-risk plaque morphological characteristics on the causative lesion precursors of acute coronary syndromes obtained from a sample study are shown.

[0045] [Figure 9E] 1 shows an example of Kaplan-Meier curves of acute coronary syndrome occurrence in patients with and without high-risk plaque morphology lesions obtained from a sample study.

[0046] [Figure 9F] 1 is a flow chart illustrating an exemplary embodiment of a system, device, and method for non-invasive image-based plaque analysis and risk determination.

[0047] [Figure 9G] FIG. 1 is a block diagram illustrating an embodiment of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0048] [Figure 9H] 9H(a) illustrates exemplary LDNC deposits along a blood vessel; FIG. 9H(b) illustrates shapes or plaques or regions of plaques (e.g., crescent, leaf, round, bean); FIG. 9H(c) illustrates plaques with various values ​​of DELP (e.g., <180°, 180°-269°, 270°-359°, and 360°); and FIG. 9H(d) illustrates exemplary high-risk plaque morphologies.

[0049] [Figure 9I] 1 is a flowchart illustrating an analysis of an example test dataset validating some embodiments of the systems, apparatus, and methods herein.

[0050] [Figure 9J] FIG. 1 illustrates patient demographics stratified by occurrence of acute coronary syndrome in an example study validating some embodiments of the systems, methods, and devices described herein.

[0051] [Figure 9K] 1 shows atherosclerotic characteristics by lesion stratified by non-culprit and culprit lesion precursors in an example study validating some embodiments of the systems, methods, and devices described herein.

[0052] [Figure 9L] 1 is a forest plot of hazard ratios for risk of causative lesion precursors in an example study validating some embodiments of the systems, methods, and devices described herein.

[0053] [Figure 9M] An example study validating some embodiments of the systems, methods, and devices described herein presents atherosclerotic signatures for patients stratified by the presence or absence of acute coronary syndromes.

[0054] [Figure 9N] 1 is a forest plot of hazard ratios for association with acute coronary syndrome from an example study validating some embodiments of the systems, methods, and devices described herein.

[0055] [Figure 9O] FIG. 1 shows acute coronary syndrome event-free survival in patients without and with high-risk plaque morphology lesions in a study validating some embodiments of the systems, methods, and devices described herein.

[0056] [Figure 9P] 1 is a forest plot of hazard ratios for the association between low-density non-calcified plaque and acute coronary syndromes from an exemplary study validating some embodiments of the systems, methods, and devices described herein.

[0057] [Figure 9Q] FIG. 1 shows acute coronary syndrome event-free survival in patients stratified by low-density non-calcified plaque burden from a study validating some embodiments of the systems, methods, and devices described herein.

[0058] [Figure 9R] In some embodiments, it illustrates how high-risk plaque morphologies are thought to increase the risk of acute coronary syndrome.

[0059] [Figure 10A]1 is a schematic diagram illustrating an exemplary embodiment of a system, method, and apparatus for cardiovascular risk and / or disease state assessment using image-based analysis of vascular surfaces and / or coordinates.

[0060] [Figure 10B] 1A-1C are schematic diagrams illustrating examples of one or more regions of plaque proximal to the myocardium versus the epicardium.

[0061] [Figure 10C] 1 is a flow chart illustrating an exemplary embodiment of a system, method, and apparatus for cardiovascular risk and / or disease state assessment using image-based analysis of vessel surfaces and / or coordinates.

[0062] [Figure 10D] FIG. 1 is a block diagram illustrating an embodiment or embodiments of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0063] [Figure 11A] FIG. 1 is a schematic diagram illustrating examples of one or more regions of calcified and / or non-calcified plaque that may be analyzed using an image analysis process according to one or more embodiments of the systems, methods, and devices herein to assess cardiovascular risk, disease, and / or condition.

[0064] [Figure 11B] 1 is a flow chart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using image-based analysis of non-calcified and / or calcified plaque.

[0065] [Figure 11C] 1 is a flow chart illustrating another exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using image-based analysis of non-calcified and / or calcified plaque.

[0066] [Figure 11D] 1 is a flow chart illustrating one embodiment of a system, device and method for cardiovascular risk and / or disease state assessment using image-based analysis of non-calcified plaque.

[0067] [Figure 11E] 1 is a flow chart illustrating one embodiment of a system, device and method for cardiovascular risk and / or disease state assessment using image-based analysis of non-calcified plaque.

[0068] [Figure 11F] FIG. 1 is a block diagram illustrating an embodiment or embodiments of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0069] [Figure 12A] FIG. 12A is a schematic diagram illustrating an example of one or more regions of plaque that may be analyzed using an image analysis process according to one or more embodiments of the systems, methods, and devices herein for the assessment of cardiovascular risk, disease, and / or condition.

[0070] [Figure 12B] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters. [Figure 12C] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters.

[0071] [Figure 12D]1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for developing a reference database for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters.

[0072] [Figure 12E] FIG. 1 is a block diagram illustrating an embodiment of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0073] [Figure 13A] FIG. 1 is a schematic diagram illustrating an example of an embodiment of an immersive patient-specific report regarding cardiovascular disease risk, status, diagnosis, and / or treatment that can be generated using one or more embodiments of the systems, methods, and devices described herein.

[0074] [Figure 13B] FIG. 1 is a schematic diagram illustrating an example of an embodiment(s) of an immersive patient-specific report regarding cardiovascular disease risk, status, diagnosis, and / or treatment that can be generated using one or more embodiments of the systems, methods, and devices described herein.

[0075] [Figure 13C] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for generating an immersive patient-specific report regarding cardiovascular disease risk, status, diagnosis, and / or treatment.

[0076] [Figure 13D] FIG. 1 is a block diagram illustrating an embodiment or embodiments of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0077] [Figure 14A]FIG. 1 is a schematic diagram illustrating an example of one or more regions of plaque that may be analyzed using an image analysis process according to one or more embodiments of the systems, methods, and devices herein for the assessment of cardiovascular risk, disease, and / or condition.

[0078] [Figure 14B] 1 is a plaque illustrating an example of one or more regions of the plaque that may be analyzed using an image analysis process according to one or more embodiments of the systems, methods, and devices herein for the assessment of cardiovascular risk, disease, and / or condition. [Figure 14C] 1 is a plaque illustrating an example of one or more regions of the plaque that may be analyzed using an image analysis process according to one or more embodiments of the systems, methods, and devices herein for the assessment of cardiovascular risk, disease, and / or condition.

[0079] [Figure 14D] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters. [Figure 14E] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters. [Figure 14F] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters. [Figure 14G] 1 is a flowchart illustrating an exemplary embodiment of a system, device, and method for cardiovascular risk and / or disease state assessment using modified and / or normalized image analysis-based plaque parameters.

[0080] [Figure 14H]FIG. 1 is a block diagram illustrating an embodiment or embodiments of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein.

[0081] [Figure 15A] 1 is a schematic diagram illustrating exemplary embodiments of systems, devices, and methods for non-invasive, image-based determination of fractional flow reserve (FFR) and / or ischemia.

[0082] [Figure 15B] 1 is a schematic diagram illustrating the results of a study utilizing exemplary embodiments of systems, devices, and methods for non-invasive, image-based determination of fractional flow reserve (FFR) and / or ischemia. [Figure 15C] 1 is a schematic diagram illustrating the results of a study utilizing exemplary embodiments of systems, devices, and methods for non-invasive, image-based determination of fractional flow reserve (FFR) and / or ischemia.

[0083] [Figure 15D] 1 is a schematic diagram illustrating exemplary embodiments of systems, devices, and methods for non-invasive imaging determination of fractional flow reserve (FFR) and / or ischemia.

[0084] [Figure 15E] 1 is a flow chart illustrating exemplary embodiments of systems, devices, and methods for non-invasive imaging determination of fractional flow reserve (FFR) and / or ischemia.

[0085] [Figure 15F] FIG. 1 is a block diagram illustrating an embodiment or embodiments of a computer hardware system configured to execute software to implement one or more embodiments of the systems, devices, and methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0086] Although several embodiments, examples, and illustrations are disclosed below, those skilled in the art will understand that the invention described herein extends beyond the specifically disclosed embodiments, examples, and illustrations and includes other uses of the invention, as well as obvious modifications and equivalents thereof. Embodiments of the invention are described with reference to the accompanying figures, in which like numerals refer to like elements throughout. The terminology used in the description presented herein, even when used in conjunction with detailed descriptions of certain specific embodiments of the invention, is not intended to be construed as limiting or restrictive. In addition, embodiments of the invention may be comprised of multiple novel features, and no single feature is solely responsible for its desirable attributes or essential to practicing the invention described herein.

[0087] Disclosed herein are systems, devices, and methods for non-invasive, image-based plaque analysis and risk assessment. In particular, in some embodiments, the systems, devices, and methods described herein relate to the analysis of one or more regions of plaque, such as coronary artery plaque, based on one or more distance, volume, shape, morphology, embedment, and / or axial (or dimensional) measurements. A "plaque" or "area of ​​plaque" or "one or more regions of plaque" may be referred to simply as "plaque" for ease of reference, unless otherwise indicated explicitly or by context. For example, in some embodiments, the systems, devices, and methods described herein relate to plaque analysis based on one or more of the following: the distance between the plaque and the vessel wall, the distance between the plaque and the lumen wall, the length of the plaque along the anterior-posterior axis, the length of the plaque along the lateral axis, the volume of low-density non-calcified plaque, the volume of total plaque, the ratio(s) between the volume of low-density non-calcified plaque and the volume of total plaque, the embedment of low-density non-calcified plaque, and / or the like. In some embodiments, the systems, devices, and methods described herein are configured to determine a risk of coronary artery disease (CAD), e.g., myocardial infarction (MI), based on one or more plaque analyses described herein. In some embodiments, the systems, devices, and methods described herein are configured to generate suggested treatments and / or visual representations based on the determined risk of CAD and / or one or more plaque analyses described herein.

[0088] Also disclosed herein are systems, methods, and devices for cardiovascular risk and / or condition assessment using image-based analysis. In particular, in some embodiments, the systems, devices, and methods are the basis for assessing cardiovascular risk and / or disease condition using image-based analysis of vascular surface and / or feature coordinates. In some embodiments, cardiovascular risk and / or disease condition assessments generated using the systems, methods, and devices herein can be utilized to generate patient diagnoses and / or treatment recommendations.

[0089] Also disclosed herein are systems, methods, and devices for cardiovascular risk and / or condition assessment using image-based analysis, and in some embodiments, the systems, devices, and methods relate to cardiovascular risk and / or disease condition assessment using image-based analysis of vascular surface and / or feature coordinates. In some embodiments, cardiovascular risk and / or disease condition assessments generated using the systems, methods, and devices herein may be utilized in patient diagnosis and / or generation of suggested treatments.

[0090] Also disclosed herein are systems, methods, and devices for cardiovascular risk and / or condition assessment using image-based analysis, and in some embodiments, the systems, devices, and methods relate to cardiovascular risk and / or disease and / or condition assessment using corrected and / or normalized image analysis-based plaque parameters. In some embodiments, cardiovascular risk and / or disease and / or condition assessments generated using the systems, methods, and devices herein can be utilized in patient diagnosis and / or generation of suggested treatments.

[0091] Disclosed herein are systems, methods, and devices for generating patient-specific reports regarding cardiovascular disease risk and / or status assessment, diagnosis, and / or treatment, including, for example, coronary artery disease (CAD). In particular, in some embodiments, the systems, devices, and methods are configured to generate immersive patient-specific reports regarding a patient's cardiovascular disease risk, status, diagnosis, and / or treatment. In some embodiments, the systems, devices, and methods are configured to generate immersive patient-specific reports based, at least in part, on image-based analysis of, for example, one or more plaque and / or vascular parameters. In some embodiments, the systems, devices, and methods are configured to view a patient's cardiovascular disease status or risk from a perspective within one or more of the patient's arteries. In some embodiments, the systems, devices, and methods are configured to visually display and / or track the actual or hypothetical progression of a patient's cardiovascular disease status or risk based on actual or proposed treatments from a perspective within one or more of the patient's arteries.

[0092] Disclosed herein are systems, methods, and devices for cardiovascular risk and / or condition assessment using image-based analysis, and in some embodiments, the systems, devices, and methods relate to cardiovascular risk and / or disease and / or condition assessment using normalized image analysis-based plaque parameters. In some embodiments, cardiovascular risk and / or disease and / or condition assessments generated using the systems, methods, and devices herein can be utilized in generating patient diagnoses and / or treatment recommendations.

[0093] Disclosed herein are systems, devices, and methods for non-invasive, image-based determination of fractional flow reserve (FFR) and / or ischemia. In particular, in some embodiments, the systems, devices, and methods relate to FFR and / or ischemia analysis of arteries, such as coronary arteries, aorta, and / or carotid arteries, using one or more image analysis techniques. For example, in some embodiments, the systems, methods, and devices can be configured to derive one or more stenosis and / or normal measurements from non-invasively obtainable medical images and use the same to derive an FFR and / or ischemia assessment. In some embodiments, the systems, methods, and devices can be configured to apply one or more allometric scaling laws to one or more stenosis and / or normal measurements to derive and / or generate an FFR and / or ischemia assessment.

[0094] Coronary heart disease afflicts more than 17.6 million Americans. Current trends in the treatment of cardiovascular health problems are generally twofold. First, physicians typically consider a patient's cardiovascular health from a macro level. For example, they analyze a patient's biochemistry or blood constituents or biomarkers to determine whether the patient has high levels of cholesterol components in their bloodstream. In response to high levels of cholesterol, some physicians prescribe one or more medications, such as statins, as part of a treatment plan to reduce the perceived high levels of cholesterol components in the patient's bloodstream.

[0095] Currently, in a second popular trend for treating cardiovascular health issues, physicians use angiograms to evaluate a patient's cardiovascular health and identify large blockages in the patient's various arteries. Upon finding large blockages in various arteries, physicians may perform angioplasty, guiding a balloon catheter to the narrowing point of the blood vessel. Once properly positioned, the balloon is inflated to compress or flatten plaque or fatty material against the vessel wall and / or stretch the artery, increasing blood flow through the vessel and / or to the heart. In some cases, the balloon is used to place and expand a stent within the vessel, compressing plaque and / or keeping the vessel open, allowing more blood to flow. Approximately 500,000 cardiac stent procedures are performed annually in the United States.

[0096] However, a recent $100 million federally funded study questions whether current trends in cardiovascular disease treatment are the most effective treatments for all types of patients. The recent study, which involved more than 5,000 patients with moderate to severe stable heart disease from 320 centers in 37 countries, presented new evidence suggesting that stents and bypass surgery are likely less effective than medications combined with lifestyle modifications for patients with stable heart disease. Therefore, patients with stable heart disease may be better served by forgoing invasive surgical procedures like angioplasty and bypass surgery and instead prescribing heart medications like statins and engaging in lifestyle modifications, such as regular exercise. This new treatment could impact thousands of patients worldwide. It is estimated that 500,000 cardiac stent procedures are performed annually in the United States, with one-fifth of these procedures being performed on patients with stable heart disease. Furthermore, it is estimated that 25% of the estimated 100,000 patients with stable heart disease, or approximately 23,000 people, do not experience chest pain. Thus, more than 20,000 patients per year could potentially avoid invasive surgery and its resulting complications.

[0097] A more complete understanding of a patient's cardiovascular disease can be important to determine whether a patient should forgo invasive surgery and instead opt for medical therapy and / or to develop a more effective treatment plan. Specifically, a better understanding of a patient's arterial vascular health is beneficial. For example, it is useful to understand whether a patient's plaque buildup is mostly fatty deposits or mostly calcified. In the former situation, treatment with cardiac medications such as statins may be warranted, whereas in the latter situation, the patient should undergo further regular monitoring without prescribing cardiac medications or implanting a stent. However, if the plaque buildup is significant enough to cause severe stenosis or narrowing of the ductus arteriosus, blocking blood flow to the heart muscle, invasive angioplasty with stent placement may be necessary. Sudden cardiac death is one of the leading causes of natural death in the United States, accounting for approximately 325,000 adult deaths per year and nearly half of all deaths due to cardiovascular disease. SCD is twice as prevalent in men as in women. SCD generally develops between the mid-30s and mid-40s. In over 50% of cases, sudden cardiac arrest occurs without warning.

[0098] For millions of patients suffering from heart disease, there is a need to not only know the blood chemistry or content of the blood flowing through such arteries, but also to better understand the overall health of the arteries within a patient. For example, in some embodiments of the systems, devices, and methods disclosed herein, arteries with "good" or stable plaque, or plaque consisting of hardened calcified content, are considered non-life-threatening to the patient, whereas arteries containing "bad" or unstable plaque, or plaque consisting of fatty material, are considered more life-threatening because such bad plaque may rupture within the artery, thereby releasing such fatty material into the artery. The release of such fatty material into the bloodstream can cause inflammation and lead to blood clots. A blood clot within the artery can prevent blood from being transported to the heart muscle, potentially leading to a heart attack or other cardiac events. Furthermore, in some cases, it is generally more difficult for blood to flow through fatty plaque buildup than through calcified plaque buildup. Therefore, there is a need to better understand and analyze a patient's arterial vessel walls.

[0099] Furthermore, while blood tests and drug treatment regimens are useful for mitigating cardiovascular health problems and mitigating cardiovascular events (e.g., heart attacks), such treatment methods are not perfect or flawless in that they may misidentify and / or fail to pinpoint areas of significant cardiovascular risk. For example, a simple analysis of a patient's blood chemistry likely will not identify that the patient has arteries with significant buildup of bad plaque (fatty deposits) along the vessel walls. Similarly, angiograms can help identify stenoses or narrowings, but may not clearly identify areas with significant buildup of bad plaque in the vessel walls. Such areas of bad plaque buildup within arterial vessel walls can be an indicator of a patient at high risk for cardiovascular events, such as heart attacks. In certain circumstances, areas with bad plaque may rupture, releasing fatty material into the arterial bloodstream, potentially resulting in a blood clot in the artery. A blood clot in the artery can block blood flow to heart tissue and potentially cause a heart attack. Therefore, there is a need for new techniques that can analyze arterial vessel walls and / or identify areas within arterial vessel walls that constitute plaque buildup, whether malignant or not.

[0100] In some embodiments, the systems, devices, and methods described herein are configured to utilize non-invasive medical imaging techniques, such as CT images or CCTA, which may be input into a computer system configured to automatically and / or dynamically analyze the medical images to identify one or more coronary arteries and / or plaques therein. For example, in some embodiments, the system may be configured to automatically and / or dynamically analyze the medical images using one or more machine learning and / or artificial intelligence algorithms to identify, quantify, and / or classify one or more coronary arteries and / or plaques. In some embodiments, the system may be further configured to utilize the identified, quantified, and / or classified one or more coronary arteries and / or plaques to generate a treatment plan, track disease progression, and / or generate a patient-specific medical report, e.g., using one or more artificial intelligence and / or machine learning algorithms. In some embodiments, the system may be further configured to dynamically and / or automatically generate a visualization of the identified, quantified, and / or classified one or more coronary arteries and / or plaques, e.g., in the form of a graphical user interface. Additionally, in some embodiments, to calibrate medical images obtained from different medical image scanners and / or different scanning parameters or environments, the system can be configured to utilize a normalization device consisting of one or more sections of one or more materials.

[0101] As described in further detail, the systems, devices, and methods described herein enable automated and / or dynamic quantitative analysis of various parameters related to plaque, cardiovascular arteries, and / or other structures. More specifically, in some embodiments described herein, medical images of a patient, such as coronary CT images or CCTA, can be taken at a medical facility. Rather than a physician visually inspecting or generally assessing the patient, the medical images are transmitted to a back-end main server, which in some embodiments is configured to perform one or more analyses of the medical images in a reproducible manner. Thus, in some embodiments, the systems, methods, and devices described herein can provide quantified measurements of one or more features of the coronary CT images using an automated and / or dynamic process. For example, in some embodiments, the main server system can be configured to identify one or more blood vessels, plaque, fat, and / or one or more measurements thereof from the medical images. Based on the identified features, in some embodiments, the system can be configured to generate one or more quantified measurements from the raw medical images, such as the radiodensity of one or more regions of plaque, identification of stable and / or unstable plaque, its volume, its surface area, geometric shape, its heterogeneity, and / or the like. In some embodiments, the system may also generate one or more quantified measurements of blood vessels from the raw medical images, such as, for example, diameter, volume, morphology, and / or the like. Based on the identified features and / or quantified measurements, in some embodiments, the system may be configured to generate a risk and / or disease state assessment and / or track the progression of a plaque-based disease or condition, such as, for example, atherosclerosis, stenosis, and / or ischemia, using the raw medical images. Additionally, in some embodiments, the system may be configured to generate a GUI visualization of the identified one or more features and / or quantified measurements, such as a quantized color mapping of different features.In some embodiments, the systems, devices, and methods described herein are configured to utilize medical image-based processing to assess a subject's risk of a cardiovascular event, a major adverse cardiovascular event (MACE), rapid plaque progression, and / or non-response to drug therapy. In particular, in some embodiments, the systems may be configured to automatically and / or dynamically assess such health risks of a subject by analyzing only non-invasively obtained medical images. In some embodiments, one or more of the processes may be automated using artificial intelligence (AI) and / or machine learning (ML) algorithms. In some embodiments, one or more of the processes described herein may be performed within minutes in a reproducible manner. This is in contrast to existing means today that do not produce reproducible prognoses or assessments, are time-consuming, and / or require invasive procedures. In some embodiments, the systems, methods, and devices described herein are configured and / or configured to utilize any one or more of such techniques described in U.S. Patent Application Publication No. US2021 / 0319558, which is incorporated herein by reference in its entirety.

[0102] Thus, in some embodiments, the systems, devices, and methods described herein can provide physicians and / or patients with specific quantification and / or measurement data related to a patient's plaque and / or ischemia that currently does not exist. In some embodiments, this level of detail in quantified plaque parameters from image processing and downstream analysis can provide more accurate and useful tools for assessing patient health and / or risk in entirely novel ways.

[0103] A method for identifying high-risk plaques is disclosed that utilizes volumetric characterization of coronary plaques and perivascular adipose tissue data from computed tomography (CT) scans. Volumetric characterization of coronary plaques and perivascular adipose tissue allows for the determination of the inflammatory state of the plaques from CT scans, which is useful for the diagnosis, prognosis, and treatment of coronary artery disease. While certain exemplary embodiments are illustratively shown in the drawings and described in detail herein, these embodiments are susceptible to various modifications and alternative forms. It is not intended that the exemplary embodiments be limited to the particular forms disclosed; on the contrary, the exemplary embodiments are intended to cover all modifications, equivalents, and alternatives falling within the scope of the exemplary embodiments.

[0104] Although terms such as "first," "second," etc. may be used herein to describe various elements, it should be understood that these elements are not limited by these terms. These terms are used merely to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of the exemplary embodiments. As used herein, the term "and / or" includes all combinations of one or more of the associated listed items.

[0105] The terms used herein are for the purpose of describing embodiments only and are not intended to limit example embodiments. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly dictates otherwise. It will be further understood that as used herein, the terms "comprises," "comprising," "includes," and / or "including" specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" refers not only to the individual items but also to all combinations thereof.

[0106] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the exemplary embodiments belong. Furthermore, it will be understood that terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. It should also be noted that in some alternative implementations, the functions / acts described may occur out of the order depicted in the figures. For example, two figures shown in succession may actually be performed simultaneously or may be performed in the reverse order depending on the functions / acts involved.

[0107] In the drawings, the dimensions of layers and regions may be exaggerated for clarity of illustration. Also, when a layer (or structure) is referred to as "on" another layer or structure, it will be understood that it can be directly on the other layer or substrate, or that intervening layers may be present. Furthermore, when a layer is referred to as "underlying" another layer, it will be understood that it can be directly under, or that one or more intervening layers may be present. Additionally, when a layer is referred to as being "between" two layers, it will be understood that it can be the only layer between the two layers, or that one or more intervening layers may also be present. Like reference numerals refer to like elements throughout.

[0108] [Overview of an example processing system for assessing coronary artery plaque] The present disclosure includes methods and systems that use data generated from images collected by scanning a patient's arteries to identify coronary plaques that are at high risk of causing a future heart attack or acute coronary syndrome. In particular, the relationship between perivascular coronary fat, coronary plaque, and / or coronary lumen characteristics, as well as the relationship between perivascular coronary fat, coronary plaque, and / or coronary lumen characteristics, is discussed to determine how to identify coronary plaques that are likely to contribute to future ACS, heart attack, and death. Images used to generate image data may be CT images, CCTA images, or images generated using any applicable technology capable of depicting the relative densities of coronary plaque, perivascular fat, and the coronary lumen. For example, CCTA images can be used to generate two-dimensional (2D) or volumetric (three-dimensional (3D)) image data, which can be analyzed to determine specific characteristics related to the radiodensity of coronary plaque, perivascular fat, and / or the coronary lumen. In some embodiments, the Hounsfield scale is used to provide a measure of the radiodensity of these features. Hounsfield units, as is well known, represent arbitrary units of X-ray attenuation used in CT scans. Each pixel (2D) or voxel (3D) of a feature in the image data can be assigned a radiodensity value on the Hounsfield scale, and these values ​​can be analyzed to characterize the feature.

[0109] In various embodiments, processing the image information may include: (1) Determine scan parameters (e.g., mA (milliamperes), kvP (peak kilovoltage)); (2) Determine scan image quality (e.g., noise, signal-to-noise ratio, contrast-to-noise ratio); (3) Measure scan-specific coronary artery lumen density (e.g., from a distal point on the coronary artery wall to a proximal point on the coronary artery wall to a distal point on the coronary artery, and from a central location on the coronary artery to an outer location (e.g., outer relative to radial distance from the coronary artery); (4) Measure scan-specific plaque density (e.g., from center to outer, abruptness of the change from high to low or low to high within the plaque) as a function of three-dimensional shape; and (5) Measure scan-specific perivascular coronary fat density (close to far from the artery) as a function of three-dimensional shape.

[0110] From these measurements, which are independent of commonly known features of atherosclerosis causing ischemia, several features can be determined, including but not limited to: 1. The ratio of luminal attenuation to plaque attenuation, where a volumetric model of scan-specific attenuation density gradients within the lumen adjusts for reduced luminal density across plaque lesions that are more functionally significant in terms of risk value. 2. Based on the ratio of plaque attenuation to fat attenuation, among the subset of plaques considered to be "calcified plaques," highly radiodense plaques are considered to be of low risk. 3. Ratio of lumen attenuation / plaque attenuation / fat attenuation 4. The ratio of #1-3 as a function of the 3D shape of atherosclerosis, which can include 3D texture analysis of plaque 5. The three-dimensional volumetric shape and path of the lumen along with the attenuation density from the beginning to the end of the lumen. 6.Compare the plaques before and after a particular plaque and the plaque type to learn more about the risk. 7. Determine "high-risk plaque" by "subtracting" calcified (high-density) plaque to obtain a better absolute measure of high-risk plaque (low-density plaque). In other words, this particular embodiment involves identifying calcified plaque and excluding it from further analysis of plaque for the purpose of identifying high-risk plaque. Other characteristics can also be determined.

[0111] Analyzing these features / indicators, along with other indicators, together can assess the risk of plaque contributing to future heart attack, ACS, ischemia, or death. This can be done through traditional risk score development and / or validation or through machine learning methods. Factors for analysis from metrics likely associated with heart attack, ACS, ischemia, or death include: (1) a low [bright lumen:dark plaque] ratio, (2) a low [dark plaque:bright fat] ratio, (3) a low [bright lumen:dark plaque:bright fat] ratio, and (4) a low [dark lumen:dark myocardium in one vascular region] / [lumen:myocardium in another vascular region] ratio. Improvements in the disclosed method and system include: (1) using numerical values ​​from [lumen:plaque], [plaque:fat], and [lumen:plaque:fat] ratios instead of using qualitative definitions of atherosclerotic features; (2) using scan-specific [lumen:plaque attenuation] ratios to characterize plaque; (3) using scan-specific [lumen:plaque attenuation] ratios to characterize plaque; (4) using [lumen:plaque:fat circumference] ratios to characterize plaque; and (5) integrating pre- and post-plaque volume and type as contributors to individual plaque risk.

[0112] Atherosclerotic plaque characteristics can change over time with medical treatment (colchicine and statin drugs), and while some of these drugs may slow plaque progression, they also play a crucial role in promoting plaque changes. While statin drugs slow overall plaque progression, they may actually increase the progression of calcified plaque and decrease non-calcified plaque. This change is associated with reduced heart attacks, acute coronary syndromes, and death, and the disclosed methods can be used to monitor the effect of drug therapy on plaque risk over time. These methods can also be used to identify individuals whose atherosclerotic plaque characteristics or [lumen:plaque] / [plaque:fat] / [lumen:plaque:fat] ratios indicate a greater susceptibility to rapid disease progression or malignant transformation. Additionally, these methods can be applied to a single plaque, or tracking of atherosclerosis throughout the heart can be used to monitor a patient's risk of experiencing a heart attack (rather than attempting to identify specific plaques as the cause of future heart attacks). Tracking can be achieved by automated co-registration of patient-related imaging data over a period of time.

[0113] 1 shows a schematic diagram of an example embodiment of a system 100 including a processing system 120 configured to characterize coronary artery plaque. The processing system 120 includes one or more servers (or computers) 105, each configured with one or more processors. The processing system 120 includes a non-transitory computer memory component for storing data and a non-transitory computer memory component for storing instructions executed by a data communication interface of the one or more processors, the instructions configuring the one or more processors to perform a method for analyzing image information. A more detailed example of a server / computer 105 is described with reference to FIG. 9G.

[0114] System 100 also includes a network. Processing system 120 is in communication with network 125. Network 125 may include, at least as part of network 125, the Internet, a wide area network (WAN), a wireless network, etc. In some embodiments, processing system 120 is part of a "cloud" implementation and may be located anywhere in communication with network 125. In some embodiments, processing system 120 is located in the same geographic proximity as an imaging facility that captures and stores patient image data. In other embodiments, processing system 120 is located remotely from where the patient image data is generated or stored.

[0115] FIG. 1 also illustrates various computer systems and devices 130 (e.g., at an imaging facility) in system 100 that are associated with generating patient image data and are also connected to network 125. One or more of devices 130 may be at an imaging facility that generates images of a patient's arteries, at a medical facility (e.g., a hospital, a doctor's office, etc.), or may be a personal computing device of a patient or caregiver. For example, as shown in FIG. 1, an imaging facility server (or computer) 130A may be connected to network 125. In this example, an imaging facility scanner 130B may also be connected to network 125. One or more other computing devices may also be connected to network 125. For example, laptop 130C, personal computer 130D, and / or image information storage system 130E may also be connected to network 125 and communicate with processing system 120 and each other via network 125.

[0116] In some examples, scanner 130B can be a computed tomography (CT) scanner, which uses a rotating X-ray tube and an array of detectors to measure the attenuation of X-rays by different tissues in the body and form corresponding images. In another example, scanner 130B can use a rotating tube ("spiral CT") in which the entire X-ray tube and detectors rotate around a central axis of the scanned area. In another example, scanner 130B can utilize electron beam tomography (EBT). In another example, scanner 130B can be a dual-source CT scanner having two X-ray tube systems. The methods and systems described herein can also use images from other CT scanners. In some examples, scanner 130B is a photon-counting CT scanner, a spectral CT scanner, or a dual-energy CT scanner. Photon-counting CT scanners, spectral CT scanners, or dual-energy CT scanners help provide more detailed, high-resolution images that better show small blood vessels, plaque, and other vascular pathologies, allowing for the determination of absolute material density rather than relative density. Generally, photon-counting CT scanners use X-ray detectors to count photons, quantify their energy, and determine the number of photons counted within several discrete energy bins, resulting in higher contrast-to-noise ratios, improved spatial resolution, and spectral imaging compared to conventional CT scanners. Each registered photon is assigned to a specific bin according to its energy, and each pixel measures a histogram of the incident X-ray spectrum. This spectral information has several advantages. First, it can be used to quantitatively determine the material composition of each pixel in the reconstructed CT image, as opposed to the estimated mean linear attenuation coefficient obtained in conventional CT scans. The spectral / energy information can be used to remove beam-hardening artifacts, which occur due to the higher linear attenuation of many materials, shifting the mean energy of the X-ray spectrum to higher energies. Additionally, the use of two or more energy bins allows for the identification of specific objects (e.g., bone, calcification, contrast agent, tissue).In some embodiments, images generated using a photon-counting CT scanner allow for the evaluation of plaque not only at different monochromatic energies but also at different polychromatic spectra (e.g., 100 kVp, 120 kVp, 140 kVp, etc.), which allows for different definitions of non-calcified and calcified plaque compared to conventional CT scanners. Spectral CT scanners perform CT scans using different X-ray wavelengths (or energies). Dual-energy CT scanners use separate X-ray energies to detect two different energy ranges. As an example, a dual-energy CT scanner (also known as spectral CT) can use an X-ray detector with separate layers to detect two different energy ranges ("dual layer"). In another example, a dual-energy CT scanner can use a single scanner to scan twice using two different energy levels (e.g., electronic kVp switching). The images detected at each different energy level can be combined to form an image, or the images can be used separately to evaluate a patient's medical condition. In addition to providing absolute material density, photon-counting CT scanners also allow for the evaluation of images that are "monochromatic," as opposed to typical CT, which uses a polychromatic spectrum of light. As discussed above, features (e.g., low-density noncalcified plaque, calcified plaque, noncalcified plaque) visualized in images generated using a photon-counting, spectral, or dual-energy CT scanner may have different radioactive densities than those visualized in images generated from a conventional CT scanner, i.e., such images may affect or alter the definition of calcified and noncalcified plaque. However, the radioactive densities or other features of calcified and noncalcified plaque visualized in images generated from a photon-counting, spectral, or dual-energy CT scanner can be normalized to correspond to the densities of a conventional CT scanner and the densities disclosed herein.Thus, the radiation densities disclosed herein can be directly correlated to the radiation densities of images produced by a photon-counting CT scanner, a spectral CT scanner, or a dual-energy CT scanner, such that the systems and methods, analyses, plaque densities, etc. disclosed herein are directly applicable to images produced from a photon-counting CT scanner, a spectral CT scanner, or a dual-energy CT scanner, normalized to the radiation density of an equivalent conventional CT scanner.

[0117] Information communicated from device 130 to processing system 120 via network 125 may include image information 135. In various embodiments, image information 135 may include 2D or 3D image data of the patient, scan information related to the image data, patient information, and other image or image-related information related to the patient. For example, image information may include patient information including one or more characteristics of the patient, such as age, sex, body mass index (BMI), medications, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, body type (e.g., "body size" or "body type," which can be based on a wide range of factors), medical images, diabetes, hypertension, history of coronary artery disease (CAD), dietary habits, medication history, family history of disease, information related to other previously collected image information, exercise habits, drinking habits, lifestyle information, test results, etc. In some embodiments, image information includes patient identification information, such as the patient's name, the patient's address, driver's license number, social security number, or another indicator of the patient's identity. Once the processing system 120 analyzes the image information 135, information related to the patient 140 may be communicated from the processing system 120 to the devices 130 via the network 125. The patient information 140 may include, for example, a patient report. The patient information 140 may also include various patient information available from a patient portal that may be accessed by one of the devices 130.

[0118] In some embodiments, image information, consisting of multiple images of a patient's coronary arteries and patient information / characteristics, may be provided from one or more devices 130 to one or more servers 105 of processing system 120 via network 125. Processing system 120 is configured to generate coronary artery information using the multiple images of the patient's coronary arteries to generate two-dimensional and / or three-dimensional data representations of the patient's coronary arteries. Processing system 120 then analyzes the data representations to generate a patient report documenting the patient's health status and risks associated with coronary plaque. The patient report may include images of the patient's arteries and visual depictions of types of coronary plaque within or near the coronary arteries. Machine learning or other artificial intelligence techniques may be used to compare the data representations of the patient's coronary arteries with data representations of other patients (e.g., stored in a database) to determine additional information regarding the patient's health. For example, the state of specific plaque in the patient's coronary arteries may determine the patient's likelihood of suffering a heart attack or other adverse coronary events. Additional information, for example, regarding the patient's CAD risk may also be determined.

[0119] FIG. 2 is a schematic diagram illustrating an example of myocardium 225 and its coronary arteries. The coronary arteries include a complex network of blood vessels ranging from large arteries to arterioles, capillaries, veins, and venules. FIG. 1 depicts a model 220 of a portion of the coronary artery system that circulates blood to and within the heart, including multiple coronary arteries, e.g., the left anterior descending (LAD) artery 215, the left circumflex (LCX) artery 220, and the aorta 240, which supplies blood to the right coronary artery (RCA) 230, described further below. The coronary arteries supply blood to the myocardium 225. Like all other tissues in the body, the myocardium 225 requires oxygen-rich blood to function. It must also transport away oxygen-depleted blood. The coronary arteries wrap around the outside of the myocardium 225. Smaller branches penetrate the myocardium 225 to transport blood. The example methods and systems described herein can be used to determine information related to blood flowing through the coronary arteries in any vessels extending therefrom. In particular, the example methods and systems described can be used to determine various information related to one or more portions of a coronary artery in which plaque has formed, which can be used to determine the risks associated with such plaque, such as whether plaque formation poses a risk of causing an adverse event to the patient.

[0120] The right side 230 of the heart 225 is depicted on the left side (relative to the page) of Figure 2, and the left side 235 of the heart is depicted on the right side of Figure 2. The coronary arteries include the right coronary artery (RCA) 205, which extends downward from the aorta 240 along the right side 230 of the heart 225, and the left coronary artery (LMCA) 210, which extends downward from the aorta 240 on the left side 235 of the heart 225. The RCA 205 supplies blood to the right ventricle, right atrium, and the SA (sinoatrial) node and AV (atrioventricular) node, which regulate the rhythm of the heart. The RCA 205 branches into smaller branches, such as the right posterior descending artery and the acute marginal artery. Together with the left anterior descending artery 215, the RCA 205 serves to supply blood to the middle section, or septum, of the heart.

[0121] The LMCA 210 branches into two arteries: the left anterior interventricular coronary artery (also known as the left anterior descending (LAD) artery 215) and the left circumflex coronary artery 220. The LAD artery 215 supplies blood to the anterior left side of the heart. Blockage of the LAD artery 215 is often referred to as a widow's infarction. The circumflex branch of the left coronary artery 220 surrounds the myocardium. The circumflex branch of the left coronary artery 220 courses first to the left and then to the right along the left portion of the coronary groove, nearly to the posterior longitudinal groove, supplying blood to the outside and back of the heart.

[0122] 3 shows an example series of images generated from a scan along a coronary artery, including selected images of a portion of the coronary artery, and how the image data may correspond to values ​​on the Hounsfield scale. In addition to obtaining image data, as described with reference to FIG. 1, scan information, including metrics related to the image data, and patient information, including patient characteristics, may also be collected.

[0123] A portion of a heart 225, the LMCA 210, and the LAD artery 215 are shown in the example of FIG. 3. A set of images 305 can be acquired along a portion of the LMCA 210 and the LAD artery 215, in this example from a first point 301 on the LMCA 210 to a second point 302 on the LAD artery 215. In some examples, the image data can be obtained using non-invasive imaging techniques. For example, CCTA image data can be generated using a scanner to create images of the heart, including the coronary arteries and other blood vessels extending therefrom. The acquired CCTA image data can then be used to generate a three-dimensional image model of features included in the CCTA image data (e.g., the right coronary artery 205, the left coronary artery 210, the left anterior descending artery 215, the circumflex branch of the left coronary artery 220, the aorta 240, and other blood vessels associated with the heart that appear in the image data).

[0124] In various embodiments, different imaging methods can be used to collect image data. For example, ultrasound or magnetic resonance imaging (MRI) can be used. In some embodiments, the imaging method includes the use of a contrast agent to help identify the structure of the coronary arteries, which is injected into the patient before the imaging procedure. Various imaging methods may have advantages and disadvantages, including resolution and suitability for imaging the coronary arteries. Imaging methods that can be used to collect coronary artery image data are constantly improving as improvements in hardware (e.g., sensors and emitters) and software are made. The disclosed systems and methods contemplate the use of CCTA image data and / or any other type of image data that can provide or convert a representative 3D depiction of the coronary arteries, plaque contained therein, and perivascular fat located proximate to the coronary arteries, including plaque, to obtain attenuation or radiodensity values ​​for the coronary arteries, plaque, and / or perivascular fat.

[0125] With further reference to FIG. 3 , a particular image 310 of image data 305 is shown, representing an image of a portion of the left anterior descending artery 215. Image 310 contains image information, and the smallest point of information manipulated by the system, generally referred to herein as a pixel, is, for example, pixel 315 of image 310. The resolution of the imaging system used to capture the image data affects the size of the smallest feature discernible in the image. Furthermore, subsequent manipulation of the image may affect the dimensions of the pixel. As an example, digital image 310 may include 4,000 pixels in each row and 3,000 pixels in each column. Pixel 315, and each pixel in image data 310 and image data 305, may be associated with a radiodensity value corresponding to the density of the pixel in the image. An exemplary mapping of pixel 315 to a point on the Hounsfield scale 320 is shown in FIG. 3 . Hounsfield scale 320 is a quantitative scale for describing radiodensity. The Hounsfield unit scale linearly transforms the original linear attenuation coefficient measurements to one in which the radiometric density of distilled water at standard pressure and temperature is defined as zero Hounsfield units (HU) and the radiometric density of air at standard pressure and temperature is defined as -1000 HU. While Figure 3 shows an example of mapping pixels 315 of image 310 to points on the Hounsfield scale 320, such association of pixels with radiometric density values ​​can also be done with 3D data, such as after the image data 305 has been used to generate a three-dimensional representation of the coronary arteries.

[0126] Once the data has been acquired and rendered into a three-dimensional representation, various processes can be performed on the data to identify an analysis region. For example, a three-dimensional representation of a coronary artery may be segmented to define multiple portions of the artery and identified as such in the data. In some embodiments, the data may be filtered (e.g., smoothed) by various methods to remove anomalies that are the result of scanning or various other errors. Various known methods for segmenting and smoothing D data may be used, and therefore, for the sake of brevity of this disclosure, will not be described in further detail herein.

[0127] 4A is a block diagram illustrating a computer system 400 in which various embodiments may be implemented. The computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor or processors 404 coupled with the bus 402 for processing information. The hardware processor 404 may be, for example, one or more general-purpose microprocessors.

[0128] Computer system 400 also includes a main memory 406, such as a random access memory (RAM), cache, and / or other dynamic storage device, coupled to bus 402 for storing information and instructions executed by processor 404. Main memory 406 may also be used to store temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in a storage medium accessed by processor 404, make computer system 400 a special-purpose machine customized to perform the operations specified in the instructions. Main memory 406 may include, for example, instructions for analyzing image information to determine characteristics of coronary arteries (e.g., plaque, perivascular fat, and coronary arteries) and generating a patient report including information characterizing aspects of the patient's health related to the coronary arteries. For example, one or more metrics may be determined, including one or more of the slope / gradient of a feature, maximum density, minimum density, the ratio of the slope of one feature to the slope of another feature, the ratio of the maximum density of one feature to the maximum density of another feature, the ratio of the minimum density of a feature to the minimum density of the same feature, or the ratio of the minimum density of a feature to the maximum density of another feature.

[0129] Computer system 400 further includes a read-only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk, optical disk, or USB thumb drive (flash drive), is provided and coupled to bus 402 for storing information and instructions.

[0130] The computer system 400 can be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT) or LCD display (or touch screen), for displaying information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 for communicating information and command selections to the processor 404. Another type of user input device is a cursor control 416, such as a mouse, trackball, or cursor direction keys, for communicating directional information and command selections to the processor 404 and for controlling cursor movement on the display 412. This input device typically has two degrees of freedom, a first axis (e.g., x) and a second axis (e.g., y), allowing the device to specify a position in a plane. In some embodiments, the same directional information and command selections as cursor control may be implemented via receiving touches on a cursorless touchscreen.

[0131] Computer system 400 may include a user interface module for implementing a GUI, which may be stored on mass storage as computer-executable program instructions executed by computer device(s). Computer system 400 may further implement the techniques described herein using customized hardwired logic, one or more ASICs or FPGAs, firmware, and / or program logic that, in combination with the computer system, causes or programs computer system 400 to become a special-purpose machine, as described below. According to one embodiment, the techniques herein are performed by computer system 400 in response to processor(s) 404 executing one or more sequences of one or more computer-readable program instructions contained in main memory 406. Such instructions may be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor(s) 404 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0132] Various forms of computer-readable storage media may be involved in carrying one or more sequences of one or more computer-readable program instructions to the processor 404 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system 400 may receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector may receive the data carried in the infrared signal and appropriate circuitry may place the data on the bus 402. The bus 402 carries the data to the main memory 406, from which the processor 404 retrieves and executes the instructions. The instructions received by the main memory 406 may optionally be stored on a storage device 410 either before or after execution by the processor 404.

[0133] Computer system 400 also includes a communication interface 418 coupled to bus 402. The communication interface 418 provides a two-way data communication coupling to a network link 420 that is connected to a local network 422. For example, the communication interface 418 may be an Integrated Services Digital Network (ISDN) card, a cable modem, a satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 418 may be a local area network (LAN) card to provide a data communication connection to a corresponding LAN (or a WAN component for communicating with a WAN). Wireless links may also be implemented. In any such implementation, the communication interface 418 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0134] Network link 420 typically provides data communication through one or more networks to other data devices. For example, network link 420 may provide a connection through local network 422 to a host computer 424 or to data equipment operated by an Internet Service Provider (ISP) 426. ISP 426 provides data communication services through the worldwide packet data communication network now commonly referred to as the "Internet" 428. Local network 422 and Internet 428 both use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks, the signals on network link 420, and the signals through communication interface 418, which carry the digital data to and from computer system 400, are examples of transmission media.

[0135] Computer system 400 can send messages and receive data, including program code, through the network(s), network link 420 and communication interface 418. In the Internet example, a server 430 might transmit a requested code for an application program through Internet 428, ISP 426, local network 422 and communication interface 418.

[0136] The received code may be executed by processor 404 as it is received, and / or stored in storage device 410, or other non-volatile storage for later execution.

[0137] Thus, in an embodiment, the computer system 105 includes a non-transitory computer storage medium storage device 410 configured to store at least patient image information. The computer system 105 may also include a non-transitory computer storage medium storage that stores instructions for the one or more processors 404 to execute a process (e.g., a method) for characterizing coronary plaque tissue data and perivascular tissue data using image data collected from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values ​​of coronary plaque and perivascular tissue located adjacent to the coronary plaque. By executing the instructions, the one or more processors 404 can quantify radio density in regions of coronary plaque in the image data; quantify radio density in at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data; determine a gradient of the quantified radio density values ​​in the coronary plaque and the corresponding perivascular tissue; determine a ratio of the quantified radio density values ​​in the coronary plaque and the corresponding perivascular tissue; and characterize the coronary plaque by analyzing one or more of the gradient of the quantified radio density values ​​in the coronary plaque and the corresponding perivascular tissue, or the ratio of the radio density values ​​of the coronary plaque and the corresponding perivascular tissue.

[0138] Various embodiments of the present disclosure may be systems, methods, and / or computer program products at any possible level of technical detail. A computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a computer processor to execute aspects of the present disclosure. For example, the functions described herein may be performed by software instructions executed by one or more hardware processors and / or any other suitable computing devices, and / or in response to the software instructions being executed by one or more hardware processors and / or any other suitable computing devices. The software instructions and / or other executable code may be read from a computer-readable storage medium (or medium).

[0139] A computer-readable storage medium may be a tangible device capable of holding and storing data and / or instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but is not limited to, an electronic storage medium (including any volatile and / or non-volatile electronic storage device), a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, solid-state drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge structures in grooves having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media is not to be construed as a transitory signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted over electrical wires.

[0140] The computer-readable program instructions described herein can be downloaded to each computing / processing device from a computer-readable storage medium or to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may be comprised of copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium in each computing / processing device.

[0141] Computer-readable program instructions (also referred to herein as, for example, “code,” “instructions,” “modules,” “applications,” “software applications,” etc.) for performing operations of the present disclosure may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as the “C” programming language. The computer-readable program instructions may be callable from other instructions or from themselves and / or may be called in response to a detected event or interrupt. Computer-readable program instructions configured to execute on a computing device may be provided on a computer-readable storage medium and / or as a digital download (and may originally be stored in a compressed or installable format that requires installation, decompression, or decryption before execution) and then stored on a computer-readable storage medium. Such computer-readable program instructions may be stored, partially or entirely, in a memory device (e.g., a computer-readable storage medium) of the executing computing device for execution by the computing device. The computer-readable program instructions may execute entirely on the user's computer (e.g., the executing computing device), partially on the user's computer as a stand-alone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server.In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may utilize the state information of the computer-readable program instructions to execute the computer-readable program instructions and personalize the electronic circuitry to carry out aspects of the present disclosure.

[0142] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0143] These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to manufacture a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer-readable program instructions can also be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, and in such a way, the computer-readable storage medium having instructions stored therein constitutes an article of manufacture containing instructions that implement an aspect of the function / act specified in the flowchart(s) and / or block diagram(s) block or blocks.

[0144] Computer-readable program instructions may be loaded into a computer, other programmable data processing device, or other device to cause the computer, other programmable device, or other device to perform a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable device, or other device perform the function / acts specified in the flowchart and / or block diagram block or blocks. For example, the instructions may initially reside on a magnetic disk or solid-state drive of a remote computer. The remote computer may load the instructions and / or modules into its dynamic memory and transmit the instructions over a telephone, cable, or optical line using a modem. A modem local to the server computing system may receive the data over the telephone / cable / optical line and, using a converter device including appropriate circuitry, place the data on a bus. The bus carries the data to memory, from which a processor retrieves and executes the instructions. The instructions received by memory are optionally stored on a storage device (e.g., a solid-state drive) either before or after execution by a computer processor.

[0145] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, which constitute one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. Moreover, in some implementations, certain blocks may be omitted. Also, the methods and processes described herein are not limited to a particular order, and the associated blocks or states may be executed in any other order as appropriate.

[0146] 4B is a block diagram illustrating an example of representative instructions that may be executed by one or more computer hardware processors in one or more computer modules in a representative processing system (computer system) 120 that may implement various embodiments described herein. As shown in FIG. 1, processing system 120 may be implemented in one computer (e.g., a server) or two or more computers (two or more servers). While instructions are represented in FIG. 4B as being in seven modules 450, 455, 460, 465, 470, 475, and 480, in various implementations, executable instructions may be in fewer modules, including one module, or in more modules.

[0147] Processing system 120 includes image information stored in storage device 410, which may come from network 125 shown in FIG. 1 . The image information may include image data, scan information, and / or patient data. In this example, storage device 410 also includes stored plaque information of other patients. For example, the stored plaque information of other patients may be stored in a database on storage device 410. In another example, the stored plaque information of other patients is stored in a storage device in communication with processing system 120. The stored plaque information of other patients may be a collection of information from one, tens, hundreds, thousands, tens of thousands, hundreds of thousands, millions, or more patients.

[0148] The information for each patient may include characteristics of the patient's plaque, such as the density or density gradient of the plaque, and the location of the plaque relative to nearby or adjacent perivascular tissue. The information for each patient may include patient information. For example, the information may include one or more of: gender, age, body mass index (BMI), medications, blood pressure, heart rate, weight, height, race, build, smoking history, medical history or diagnosed history of diabetes, medical history or diagnosed history of hypertension, medical history of coronary artery disease, family history of coronary artery disease and / or other diseases, or one or more test results (e.g., blood test results). The information for each patient may include scan information. For example, the information may include one or more of the following: contrast-to-noise ratio, signal-to-noise ratio, tube current, tube voltage, contrast type, contrast volume, flow rate, flow duration, slice thickness, slice spacing, pitch, vasodilators, beta-blockers, recon option (repeated projection or filtered backprojection), recon type (standard or high-resolution), field of view, rotation speed, gating (fluoroscopic triggering or retrospective gating), stent, heart rate, or blood pressure. Cardiac information may also be included for each patient. For example, the information may include plaque characterization, including one or more of density, volume, geometry (shape), location, remodeling, baseline anatomy (diameter, length), compartment (medial, lateral, medial), stenosis (diameter, area), myocardial mass, plaque volume, and / or plaque composition, texture, or uniformity.

[0149] The processing system 120 also includes memory 406, 408, which may be the processing system's main memory or read-only memory (ROM). The memory 406, 408 stores instructions executable by one or more computer hardware processors 404 (collectively referred to herein as "modules") for characterizing coronary artery plaque. With reference to this figure, the memory 406, 408 will be collectively referred to as memory 406 for simplicity. Examples of functions performed by the executable instructions are described below.

[0150] The memory 406 includes a module 450 that generates, from the image data stored in the storage device 410, a 2-D or 3-D representation of the coronary arteries, including plaque, and perivascular tissue adjacent to or proximal to the plaque-containing coronary arteries. The generation of the 2-D or 3-D representation of the coronary arteries can be performed from a series of images 305 (e.g., CCTA images), as described above with reference to FIG. 3. Once the representation of the coronary arteries is generated, different portions or segments of the coronary arteries can be identified for evaluation. For example, portions of interest in the right coronary artery 205, the left anterior descending artery 215, or the circumflex branch of the left coronary artery 220 can be identified as analysis regions (regions of interest) based on input from a user or based on features determined from the representation of the coronary arteries (plaque).

[0151] In module 460, one or more computer hardware processors quantify the radioactivity intensity in the region of the coronary plaque. For example, the radioactivity intensity in the region of the coronary plaque is set to a value on the Hounsfield scale. In module 465, one or more computer hardware processors quantify the radioactivity density of perivascular tissue adjacent to the coronary plaque and quantify the radioactivity density value of the lumen of the vessel of interest. In module 470, one or more computer hardware processors determine the gradient of the radioactivity density values ​​of the plaque, perivascular tissue, and / or lumen. In module 475, one or more computer hardware processors determine one or more ratios of the radioactivity density values ​​of the plaque, perivascular tissue, and / or lumen. Next, in module 480, one or more computer hardware processors characterize the coronary plaque using the gradient of the plaque, perivascular tissue, and / or lumen and / or characterize the ratio of the radioactivity density value of the coronary plaque to the perivascular tissue and / or lumen, including comparing the gradient and / or ratio with a database containing information on the gradient and ratio of plaques of other patients. For example, the gradients and / or ratios are compared to patient data stored in memory device 410. Determining the gradients and ratios of plaque, perivascular tissue, and lumen is described in more detail with reference to Figures 6-12.

[0152] FIG. 5A shows an example flowchart of a process 500 for analyzing coronary artery plaque. In block 505, process 500 generates image information including image data related to the coronary arteries. In various embodiments, this may be performed by scanner 130B (FIG. 1). In block 510, a processing system may receive the image information via network 125 (FIG. 1), where the image information includes the image data. In block 515, process 500 generates a 3D representation of the coronary arteries, including perivascular fat and plaque, on the processing system. The functions of blocks 505, 510, and 515 may be performed using, for example, various imaging techniques (e.g., CCTA) to generate the image data, communication techniques to transfer the data over a network, and processing techniques to generate the 3D representation of the coronary arteries from the image data.

[0153] In block 520, the processing system performs a portion of step 500 to analyze coronary artery plaque, which is described in further detail with reference to step 550 of Figure 5B. Additional details of this process of analyzing coronary artery plaque are described with reference to Figures 6-12.

[0154] FIG. 5B shows an example of a flowchart that expands on a portion of the flowchart of FIG. 5A for determining characteristics of coronary plaque. Referring now to FIG. 5B, in block 555, step 550 can utilize one or more processors 404 to quantify radioactive density in regions of coronary plaque. In block 560, step 550 can utilize one or more processors 404 to quantify radioactive intensity in at least one region of corresponding perivascular tissue (meaning perivascular tissue adjacent to the coronary plaque) in the image data. In block 565, step 550 determines gradients of the quantified radioactive density values ​​in the coronary plaque and the corresponding perivascular tissue. One or more processors 404 can be instrumental in determining these gradients. In block 570, step 550 can determine a ratio between the quantified radioactive intensity values ​​in the coronary plaque and the corresponding perivascular tissue, e.g., perivascular tissue adjacent to the coronary plaque. One or more processors 404 can determine these ratios. At block 575, process 550 may utilize one or more processors 404 to characterize the coronary plaque by analyzing one or more of the gradient of the quantified radiodensity values ​​in the coronary plaque and corresponding perivascular tissue, or the ratio of the radiodensity values ​​of the coronary plaque to the radiodensity values ​​of the corresponding perivascular tissue, after which process 550 may return to process 500, as indicated by circle A.

[0155] Referring again to FIG. 5A , at block 525, process 500 may compare the determined coronary plaque information for a particular patient with stored patient data, such as, for example, patient data stored in storage device 410. Figure 5 shows an example of coronary plaque information for a particular patient that may be compared with stored patient data. One or more pieces of scan information may be used to better understand and / or help determine the patient's coronary plaque information. Additionally, when comparing the coronary plaque information for a particular patient with previously stored coronary plaque information, for example, one or more characteristics of the patient may be compared. In some examples, the coronary plaque information for the patient being examined may be compared or analyzed with reference to patients having one or more of the same or similar patient characteristics. For example, the patient being examined may be compared with patients having the same or similar characteristics, such as gender, age, BMI, medications, blood pressure, heart rate, weight, height, race, build, smoking, diabetes, hypertension, history of coronary artery disease, family history, test results, etc. Such comparisons may be performed by various means, such as machine learning and / or artificial intelligence techniques. In some examples, a neural network is used to compare the patient's coronary artery information with that of a large number (e.g., 10,000 or more) of other patients, and for those patients with similar patient information and similar cardiac information, a plaque risk assessment for the examined patient can be determined.

[0156] Figure 6 shows an example of a region indicated by box 605 in which the contrast attenuation pattern in a proximal portion of a coronary artery lumen can be analyzed, with box 605 extending from a central region of blood vessel 665 toward vessel wall 661. Figure 6 shows another example of a region indicated by box 652 in which the contrast attenuation pattern in a portion of a coronary artery lumen of blood vessel 665 can be analyzed, with box 652 extending longitudinally relative to blood vessel 665 from a central region of blood vessel 665 toward vessel wall 661. Figure 6 further shows an example of a region indicated by box 662 in which the contrast attenuation pattern of a portion of the lumen, a portion of fibrous plaque 610, and a portion of plaque 620 can be analyzed, with box 662 thus covering a portion of blood vessel 665, a portion of fibrous plaque 610, and a portion of plaque 620. 6 further illustrates an example of a region indicated by box 642, which extends across the portion of plaque 635 and the density of fat 640 disposed adjacent to plaque 635, in which the contrast attenuation pattern of the portion of plaque 635 and the portion of fat 640 disposed adjacent to plaque 635 may be analyzed. Information determined by analyzing various aspects of the density of coronary artery characteristics (e.g., the lumen, plaque, and / or perivascular fat) can be combined with other information to determine characteristics of the patient's arteries. In some examples, the determined information may include, for either the lumen, plaque, or perivascular fat, the slope / gradient of the feature, the maximum density, the minimum density, the ratio of the slope of the density of one feature to the slope of the density of another feature, the ratio of the maximum density of one feature to the maximum density of another feature, the ratio of the minimum density of one feature to the minimum density of the same feature, the directionality of the density ratio, which may include one or more of the density ratios, for example, the density ratio of features facing one direction to features facing the opposite direction (e.g., the radial density ratio of features facing the inside of the myocardium to features facing the outside of the pericardium), or the ratio of the minimum density of one feature to the maximum density of another feature. Such determined information may indicate a clear difference in plaque risk in a patient. In some examples, the determined information (e.g., as described above) may be used in conjunction with the percentage stenosis diameter to determine the characteristics of the patient's arteries.

[0157] Still referring to FIG. 6 , in one example of the directionality of the radiation intensity ratio, the density of a portion of the necrotic core plaque 615 relative to the density of a portion of the blood vessel 665 (e.g., plaque:inward ratio) can be determined, which can indicate a certain risk of plaque. In another example of the directionality of the radiation intensity ratio, the density of a portion of the blood vessel 665 relative to the density of the necrotic core plaque 615 (e.g., blood vessel:outward ratio) can be determined, which can indicate a certain risk of plaque. In another example, the density ratio of the necrotic core plaque 615 relative to the density of a portion of the blood vessel 665 (e.g., plaque:inward ratio) can be compared to the density ratio of the necrotic core plaque 615 relative to the fibrous plaque 620 (e.g., plaque:outward ratio) which can indicate a certain risk of plaque. In other examples, adjacently located features can be used to determine inward and / or outward radiation density values ​​that can be used to indicate the risk associated with the plaque. Such ratios can provide a clear distinction in plaque risk. Various embodiments of the directional radiodensity values ​​and / or directional radiodensity ratios can be included with any of the other information described herein to indicate plaque risk.

[0158] Compartment size can also be used to indicate plaque-associated risk. For example, the assessment of plaque-associated risk can be based at least in part on compartment size, with the radiodensity ratio affecting the risk assessment and a function of compartment size also affecting the risk assessment. The presence of plaque in a patient with a high plaque:fat ratio may indicate high-risk plaque, but a small amount of plaque (e.g., a small compartment of plaque) would be at higher risk than a large compartment of the same plaque with the same radiodensity ratio of plaque to fat. In one embodiment, the size (e.g., volume) of compartment characteristics (e.g., of the lumen, plaque, perivascular tissue (fat), and myocardium) can be determined, and the radiodensity ratio can be determined, which can then be weighted based on compartment size. For example, a larger compartment can have a higher ratio weight, making it more indicative of plaque-associated risk. Similarly, a smaller compartment can have a lower ratio weight, making it less indicative of plaque-associated risk. In some embodiments, only plaque compartment size is used to weight (or adjust) the ratio. In some embodiments, the compartment sizes of both features used in the radiation density ratio may be used to weight the ratio to determine the resulting risk. In some embodiments, the compartment sizes of any of the plaque, lumen, perivascular tissue, and myocardium are used to weight (or adjust) the risk associated with the radiation intensity ratio. In embodiments, the compartment sizes of one or more of the plaque, lumen, perivascular tissue, and myocardium are used to weight the risk associated with the radiation intensity ratio. Various embodiments that use compartment size to determine plaque risk can be included along with any of the other information described herein to indicate plaque risk.

[0159] FIG. 7 illustrates the same blood vessel 665 and plaque and fat characteristics as illustrated in FIG. 6 , and further illustrates additional examples of regions of an artery, as well as plaque and / or perivascular fat near the artery, that may be analyzed to determine characteristics of a patient's artery. Such regions are depicted in FIG. 7 by rectangular boxes, similar to the diagram in FIG. 6 . While specific locations of the rectangular boxes are depicted in FIGS. 6 and 7 , these are merely examples of regions that may be analyzed. In one example, FIG. 7 illustrates a box 660 that includes a portion of a blood vessel 665, a portion of a necrotic core plaque 615, a portion of a fibrous plaque 610, a portion of a plaque 620, and a portion of a fat 625. In another example, FIG. 7 illustrates a box 655 that includes a portion of a blood vessel 665, a portion of a fibrous plaque 610, a portion of a plaque 620, a portion of a necrotic core plaque 615, and a portion of a fat 625. Box 655 may, in some cases, illustrate a general region for analysis due to the presence of three different types of plaque 610, 615, and 620 and adjacently disposed fat 625. Specific portions of the general region for analysis may be analyzed to better understand the signature formed by adjacent features. For example, FIG. 7 illustrates a general region 665 that includes box 660 (discussed above), box 673 extending across a portion of fibrous plaque 610 and plaque 620, and box 674 extending across a portion of plaque 620 and perivascular fat 625. As another example, FIG. 7 also illustrates another box 672 extending across a blood vessel 655 and a portion of necrotic core plaque 615. As a further example, FIG. 7 illustrates box 671 extending across a portion of blood vessel 665 and fat 640 juxtaposed to blood vessel 665. As a further example, FIG. 7 illustrates box 670 extending across a portion of blood vessel 665 and plaque 635. As indicated above, the patient's arterial characteristics that can be analyzed based on these features include, but are not limited to: 1. The ratio of lumen attenuation to plaque attenuation, where a volumetric model of scan-specific attenuation density gradients within the lumen adjusts for reduced lumen density throughout plaque lesions that are more functionally significant in terms of risk value. 2. Based on the ratio of plaque attenuation to fat attenuation, among the subset of plaques considered to be "calcified plaques," highly radiodense plaques are considered to be of low risk. 3. Ratio of lumen attenuation / plaque attenuation / fat attenuation. 4. The ratio of #1-3 as a function of the 3D shape of the atherosclerosis, which can include 3D texture analysis of the plaque. 5. The three-dimensional volumetric shape and path of the lumen along with the attenuation density from the beginning to the end of the lumen. 6. By looking at the plaques before and after a certain plaque and the type of plaque, you can learn more about the risk. 7. Determine "high-risk plaque" by "subtracting" calcified (high-density) plaque to obtain a better absolute measure of high-risk plaque (low-density plaque). In other words, this particular embodiment involves identifying calcified plaque and excluding it from further analysis of plaque for the purpose of identifying high-risk plaque.

[0160] In some embodiments, the systems, devices, and methods described herein can automatically and / or dynamically perform quantitative analysis of various parameters related to plaque, cardiovascular arteries, and / or other structures. For example, rather than a physician visually inspecting or generally assessing a patient, in some embodiments, medical images can be sent to a back-end main server configured to perform such analysis, advantageously in a consistent, objective, and / or reproducible manner. In some embodiments, the systems, methods, and devices described herein can provide quantified measurements of one or more features of coronary CT images using an automated and / or dynamic process. For example, in some embodiments, the main server system can be configured to identify one or more vessels, plaque, and / or fat from the medical images. Based on the identified features, in some embodiments, the system can be configured to generate one or more quantified measurements from the raw medical images, such as, for example, the density and / or radiodensity of one or more regions of plaque, identification of stable and / or unstable plaque, perivascular fat, pericoronary adipose tissue (PCAT), fat attenuation index (FAI), its volume, its surface area, its geometric shape, its heterogeneity, and / or the like. In some embodiments, the system can also generate one or more quantified measurements of the blood vessels from the raw medical images, such as diameter, volume, morphology, and / or the like.

[0161] Based on the identified features and / or quantified measurements, in some embodiments, the system can be configured to use the raw medical images to generate a risk assessment and / or track the progression of a plaque-based disease or condition, such as atherosclerosis, stenosis, ischemia, myocardial infarction, and / or major adverse cardiovascular events (MACE). As described further herein, in some embodiments, the system can perform risk assessment and / or tracking of plaque-based disease progression based on other patient information. For example, tracking of plaque-based disease progression can be performed by comparing or evaluating a patient's medical image features and patient information (e.g., age, sex, BMI, medications, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, medical history, family history of disease, etc.) with other patients' medical image features and related patient information, including outcomes over a period of time.

[0162] Additionally, in some embodiments, the system may be configured to generate a GUI visualization of one or more identified features and / or quantified measurements, such as a quantized color mapping of different features. In some embodiments, the systems, devices, and methods described herein are configured to utilize medical image-based processing to assess a subject's risk of a cardiovascular event, a major adverse cardiovascular event (MACE), rapid plaque progression, and / or non-response to drug therapy and / or lifestyle changes and / or response to other therapies and / or non-invasive procedures. In particular, in some embodiments, the system may be configured to automatically and / or dynamically assess such health risks of a subject by analyzing only non-invasively obtained medical images. In some embodiments, one or more of the processes can be automated using artificial intelligence (AI) and / or machine learning (ML) algorithms. In some embodiments, one or more of the processes described herein can be performed within minutes in a reproducible manner. This is in contrast to existing means today that do not produce reproducible prognoses or assessments and require time-consuming and / or invasive procedures.

[0163] In some embodiments, image information, including multiple images of a patient's coronary arteries and patient information / characteristics, may be provided from one or more devices over a network to one or more servers of a processing system. The processing system is configured to generate coronary artery information using the multiple images of the patient's coronary arteries to generate two-dimensional and / or three-dimensional data representations of the patient's coronary arteries. The processing system then analyzes the data representations to generate a patient report documenting the patient's health status and risks associated with coronary plaque. The patient report may include images of the patient's arteries and visual depictions of types of coronary plaque within or near the coronary arteries. Machine learning or other artificial intelligence techniques may be used to compare the data representations of the patient's coronary arteries with data representations of other patients (e.g., stored in a database) to determine additional information regarding the patient's health. In some embodiments, the artificial intelligence may be trained using a dataset of data representations of other patients to identify data correlations. For example, based on the status of specific plaque in the patient's coronary arteries, a patient's likelihood of suffering a heart attack or other adverse coronary events may be determined. Additional information, for example, regarding the patient's CAD risk may also be determined.

[0164] In some embodiments, the coronary plaque information of the patient being examined may be compared with or analyzed with reference to patients having one or more of the same or similar patient characteristics. For example, the patient being examined may be compared with patients having the same or similar characteristics of gender, age, BMI, medication, blood pressure, heart rate, weight, height, race, build, smoking, diabetes, hypertension, history of coronary artery disease, family history, and test results. Such comparisons may be performed by various means, such as machine learning and / or artificial intelligence techniques. In some examples, a neural network is used to compare the patient's coronary artery information with that of a large number (e.g., 10,000 or more) of other patients. For such patients with similar patient information and similar cardiac information, a plaque risk assessment for the patient being examined may be determined.

[0165] In some embodiments, deep learning (DL), machine learning (ML), and artificial intelligence (AI) methods can be used to analyze image information. In one example, this analysis can consist of image segmentation, feature extraction, and classification. In some embodiments, ML methods can include image feature extraction from raw data and image-based learning. In some embodiments, ML methods can receive input from a large training set and learn to ignore variations that could otherwise skew the results of the method. In some embodiments, DL can construct neural networks (NNs) with three or more layers, which can improve the accuracy of the determination. Advantageously, in some embodiments, DL can eliminate the need for data preprocessing and instead process raw data. For example, a human can input a hierarchy of important features of coronary artery image information for the ML algorithm to make a determination, while the DL algorithm can determine which features are important and use these features to make the determination. Advantageously, in some embodiments, the DL algorithm can tune itself for accuracy and precision. In some embodiments, ML and DL algorithms can perform supervised learning, unsupervised learning, and reinforcement learning.

[0166] In some embodiments, NN approaches, including convolutional neural networks (CNNs) and recurrent convolutional neural networks (RCNNs), among others, can be used to analyze information in a manner similar to the high-level cognitive functions of the human mind. In some embodiments, the NN approach involves training an object recognition system with a large number of medical images to teach it patterns in the images that correlate with specific labels. In some embodiments, the CNN can be configured as an NN in which nodes in each layer are clustered, the clusters overlap, and each cluster feeds multiple nodes in the next layer. In some embodiments, the RCNN can be configured as a CNN in which recurrent connections are incorporated into each convolutional layer. Advantageously, in some embodiments, the recurrent connections can make object recognition a dynamic process, even though the input is static.

[0167] In some embodiments, a vessel identification algorithm, a coronary artery identification algorithm, and / or a plaque identification algorithm can be trained on a plurality of medical images in which regions of one or more vessels, coronary arteries, and / or plaque have been pre-identified. Based on such training, such as using CNNs in some embodiments, the system can be configured to automatically and / or dynamically identify the presence and / or parameters of vessels, coronary arteries, and / or plaque from raw medical images. In some embodiments, the system can be configured to utilize one or more AI and / or ML algorithms to identify and / or analyze vessels or plaque, derive one or more quantification indices and / or classifications, and / or generate a treatment plan. In some embodiments, the system can be configured to utilize AI and / or ML algorithms to identify regions within an artery exhibiting plaque accumulation within, along, within, and / or outside the artery. In some embodiments, inputs to the AI ​​and / or ML algorithms may include patient images, as well as patient information (or characteristics), such as one or more of age, sex, body mass index (BMI), medications, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, body habitus (e.g., "body size" or "body type"), although "body size" or "body type" can refer to a wide range of factors, medical history, diabetes, hypertension, history of coronary artery disease (CAD), dietary habits, medication history, family history of disease, information related to other previously collected image information, exercise habits, drinking habits, lifestyle information, or test results. In examples where a NN is used, the NN can be trained using information from multiple patients, where each patient's information may include medical images and one or more patient characteristics.

[0168] In some embodiments, the system can be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically identify one or more regions of plaque using image processing. For example, in some embodiments, one or more AI and / or ML algorithms can be trained using CNNs on a set of medical images in which regions of plaque have been identified, thereby allowing the AI ​​and / or ML algorithms to automatically identify regions of plaque directly from the medical images. In some embodiments, the system can be configured to identify the vessel wall and lumen wall for each identified coronary artery in the medical images. In some embodiments, the system is then configured to determine the volume between the vessel wall and the lumen wall as plaque. In some embodiments, the system can be configured to identify regions of plaque based on radiodensity values ​​typically associated with plaque, for example, by setting a predetermined threshold or range of radiodensity values ​​typically associated with plaque, with or without normalization using a normalizer.

[0169] In some embodiments, one or more vascular morphological parameters and / or plaque parameters may constitute quantified parameters derived from medical images. For example, in some embodiments, the system may be configured to determine one or more vascular morphological parameters and / or plaque parameters using AI and / or ML algorithms or other algorithms. As another example, in some embodiments, the system may be configured to determine one or more vascular morphological parameters, such as a classification of arterial remodeling due to plaque, which may further include positive arterial remodeling, negative arterial remodeling, and / or intermediate arterial remodeling. In some embodiments, the classification of arterial remodeling is determined based on the ratio of the maximum vessel diameter in the region of the plaque to a normal reference vessel diameter in the same region, which can be retrieved from a normal database. In some embodiments, the system may be configured to classify arterial remodeling as positive if the ratio of the maximum vessel diameter in the region of the plaque to the normal reference vessel diameter in the same region is 1.1 or greater. In some embodiments, the system may be configured to classify arterial remodeling as negative if the ratio of the maximum vessel diameter in the region of the plaque to the normal reference vessel diameter in the same region is less than 0.95. In some embodiments, the system may be configured to classify arterial remodeling as intermediate if the ratio of maximum vessel diameter to normal reference vessel diameter in the region of plaque is between 0.95 and 1.1.

[0170] In some embodiments, the system is configured to classify the subject's atherosclerosis into one or more of high risk, medium risk, or low risk based on the quantified atherosclerosis. In some embodiments, the system is configured to classify the subject's atherosclerosis based on the quantified atherosclerosis using AI, ML, and / or other algorithms. In some embodiments, the system is configured to classify the subject's atherosclerosis by combining and / or weighting one or more of surface area, volume, heterogeneity index, and radiodensity ratio of one or more regions of plaque.

[0171] In some embodiments, the system may be configured to automatically and / or dynamically identify one or more regions of fat, such as epicardial fat, in medical images, e.g., using one or more AI and / or ML algorithms. In some embodiments, the one or more AI and / or ML algorithms may be trained using CNNs on a set of medical images in which regions of fat have been identified, thereby allowing the AI ​​and / or ML algorithms to automatically identify regions of fat directly from the medical images. In some embodiments, the system may be configured to identify regions of fat based on radiodensity values ​​typically associated with fat, e.g., by setting a predetermined threshold or range of radiodensity values ​​typically associated with fat, with or without normalization using a normalizer.

[0172] In some embodiments, the system is configured to utilize AI, ML, and / or other algorithms to characterize changes in calcium scores based on one or more plaque parameters derived from medical images. For example, in some embodiments, the system may be configured to utilize AI and / or ML algorithms trained using CNNs and / or datasets of known medical images that combine identified plaque parameters with calcium scores. In some embodiments, the system may be configured to characterize changes in calcium scores by accessing a known dataset of the same stored in a database. For example, the known dataset may include a dataset of changes in calcium scores and / or medical images of other subjects and / or plaque parameters derived therefrom. In some embodiments, the system may be configured to characterize changes in calcium scores and / or determine their causes on a vessel-by-vessel, segment-by-segment, plaque-by-plaque, and / or subject-by-subject basis.

[0173] In some embodiments, the systems disclosed herein can be used to dynamically and automatically determine the type, length, diameter, gauge, strength, and / or any other stent parameters required for a particular patient based on processed medical image data, e.g., using AI, ML, and / or other algorithms.

[0174] In some embodiments, the system may be configured to utilize AI and / or ML algorithms to generate a patient-specific report, which in some embodiments may include documents, AR experiences, VR experiences, videos, and / or audio components.

[0175] FIG. 8A is a block diagram illustrating an example of a system and / or process 800 (both referred to herein as the “system” for ease of reference) for identifying patient characteristics and / or risk information using AI / ML based on non-invasively acquired patient medical images and / or patient information. Current patient medical data, including images and / or patient information, is first acquired and electronically stored in a medical data storage device 816 (e.g., cloud storage, a hard disk, etc.). The system 800 retrieves medical images and / or patient information 818 from the medical data storage 816 and, if necessary, preprocesses them, e.g., reformatting them as needed for further processing. The system 800 can also obtain a training set 822 of medical images and / or patient information from a stored dataset 820 of medical images and / or information of other patients (e.g., hundreds, thousands, tens of thousands, or hundreds of thousands or more other patients). The medical images and information of the other patients can be used to train an AI / ML algorithm 824 prior to processing the current patient's medical images and / or patient information 818, as described in further detail with reference to FIGS. 8C and 8D. In some embodiments, the AI / ML algorithm 824 may include one or more NNs, for example, as described with reference to the exemplary NN illustrated in Figure 8B. The ML / AI 824 processes the medical images and / or patient information 818 of the current patient and generates an output of identified characteristics and / or risk information 826 of the current patient.

[0176] 8B is a schematic diagram illustrating an example of a NN 812 that makes a decision 814 regarding a characteristic of a (current) patient based on inputs including a medical image 802. In some embodiments, the NN 812 can be configured to receive other inputs 804. In some embodiments, the other inputs 804 can be medical images of other patients. In some embodiments, the other inputs 804 can be medical histories of other patients. In some embodiments, the other inputs 804 can be medical histories of the (current) patient. The NN 812 can include an input layer 806. In some embodiments, the NN 812 can be configured to present training patterns to the input layer 806. In some embodiments, the NN 812 can include one or more hidden layers 808. In some embodiments, the input layer 806 can provide signals to the hidden layer 808, and the hidden layer 808 can receive signals from the input layer 806. In some embodiments, the hidden layer 808 can pass signals to the output layer 810. In some embodiments, one or more hidden layers 808 may be configured as convolutional layers (comprising neurons / nodes connected by weights, where the weights correspond to the strength of the connections between the neurons), pooling layers, fully connected layers, and / or normalization layers. In some embodiments, the NN 812 may be configured with pooling layers that combine the outputs of neuron clusters in one layer into a single neuron in the next layer. In some embodiments, max pooling and / or average pooling may be utilized. In some embodiments, max pooling may utilize the maximum value from each of the neuron clusters in the previous layer. In some embodiments, backpropagation may be utilized, and the corresponding neural network weights may be adjusted to minimize or reduce the error. In some embodiments, the loss function may comprise a binary cross-entropy loss function.

[0177] In some embodiments, the NN 812 can include an output layer 810. In some embodiments, the output layer 810 can receive signals from the hidden layer 808. In some embodiments, the output layer can generate a decision 814. In some embodiments, the NN 812 can make a decision 814 regarding a characteristic of the patient. In some embodiments, the decision 814 can include a set of plaque features. In some embodiments, the decision 814 can include the patient's risk of CAD.

[0178] 8C shows an example of a process in a flow diagram for training an artificial intelligence or machine learning model. Process 828 may be executed on a computer system. Various embodiments of such a process for training an AI or ML model may include additional features and / or exclude certain illustrated features (e.g., if a transformed dataset is accessed such that "apply transformation" in block 832 need not be performed).

[0179] 8C, in block 830, the system receives a dataset including patient health information, which may include medical images, a user survey, past test results, genetic information, and / or other patient information (e.g., height, weight, age, etc.) The dataset may also include non-health information, such as, for example, employment information, income information, transportation information, housing information, distance to a pharmacy, and / or distance to a healthcare provider.

[0180] In block 832, one or more transformations may be performed on the data. In one example, data may require transformation to conform to expected input formats, such as date formats, units (e.g., pounds vs. kilograms, Celsius vs. Fahrenheit, inches vs. centimeters, etc.), address conventions, consistent formatting, etc. In some embodiments, addresses may be converted or altered to be in a consistent format and / or conform to standards issued by the United States Postal Service or similar postal authority. In some embodiments, data may undergo transformations to prepare it for use in training AI or ML algorithms; for example, categorical data may be encoded in a particular manner. In some embodiments, nominal data may be encoded using one-hot encoding, binary encoding, feature hashing, or other suitable encoding methods. In some embodiments, ordinal data may be encoded using ordinal encoding, polynomial encoding, Helmert encoding, etc. In some embodiments, numeric data may be normalized, for example, by scaling the data to a maximum value of 1 and a minimum value of 0 or -1. These are merely examples, and one of ordinary skill in the art will readily appreciate that other transformations are possible.

[0181] At block 834, the system may create a training dataset, a tuning dataset, and a test / validation dataset from the received dataset. In some embodiments, the training dataset 836 may be used during training to determine features for forming a predictive model. In some embodiments, the tuning dataset 838 may be used to select a final model and to prevent or correct overfitting that may occur during training with the training dataset 836, as the trained model should be generally applicable to a wide range of patients. In some embodiments, the test dataset 840 may be used after training and tuning to evaluate the model. For example, in some embodiments, the test dataset 840 may be used to check whether the model is overfitting to the training dataset. In some embodiments, the system may train a model using the training dataset 836 at block 842 in a training loop 856. In some embodiments, training may be performed in a supervised, unsupervised, or partially supervised manner. In some embodiments, at 844, the system may evaluate the model according to one or more evaluation criteria. For example, in some embodiments, evaluation may include determining how often the model determines a reasonable score for a patient's risk of CAD. At 846, in some embodiments, the system may determine whether the model meets one or more evaluation criteria. In some embodiments, if the model fails evaluation, the system may tune the model at 848 using the tuning dataset 838 and repeat training 842 and evaluation 844 until the model passes evaluation at 846. In some embodiments, once the model passes evaluation at 846, the system may exit the model training loop 856. In some embodiments, the test dataset 836 is run through the trained model 842, and at block 844, the system may evaluate the results.In some embodiments, if the evaluation fails, at block 846, the system may re-enter the training loop 856 for additional training and tuning. If the model passes, the system stops the learning process, resulting in a trained model 850. In some embodiments, the training process may be modified. For example, in some embodiments, the system does not use the tuning data set 838. In some embodiments, the model does not use the test data set 840.

[0182] Although described above with respect to determining a CAD risk score, the model can be trained for use in a variety of problems.

[0183] FIG. 8D shows an example of a process for training and using an AI / ML model. In some embodiments, the process of FIG. 8D can be used for various purposes, such as determining a patient's CAD risk score or characterizing plaque. In some embodiments, a training data store 858 can store data for training the model. For example, in some embodiments, the training data store 858 can store patient medical images, as well as information about the patient's health status, age, socioeconomic status, employment status, living arrangements, transportation, etc. In some embodiments, the training data can be annotated to include information about user outcomes. For example, in some embodiments, a user's outcome can indicate whether the user had to take time off work due to illness, whether they were hospitalized, whether they visited an emergency room, whether they visited an urgent care facility, etc. In some embodiments, the training data can indicate whether the user received medication to treat the illness at home, whether they received treatment at a hospital or other medical facility, whether they did not receive treatment, etc. At block 860, in some embodiments, the system can be configured to prepare training data for use in training the model, if the training data has not been previously prepared. In some embodiments, as briefly described above, preparing the training data may include performing one or more normalization procedures, standardization procedures, etc., such as converting units (e.g., between Fahrenheit and Celsius, between inches and centimeters, between pounds and kilograms), converting dates to a standard format, converting time to a standard format, etc. In some embodiments, similar treatments or symptoms may be described or coded differently by different healthcare providers. In some embodiments, different healthcare providers may use different coding schemes. In some embodiments, even within a particular coding scheme, healthcare providers may select different codes to indicate similar information. In some embodiments, a large number of similar codes may result in coding variability.Thus, in some embodiments, a code may be changed to a different, related code. In some embodiments, a particular code may be excluded if it is not relevant to the problem the model is intended to address. In some embodiments, excluding certain data may be desirable because adding data consumes additional computational resources and increases the time it takes to train the model. However, in some embodiments, excluding may be undesirable because there may be a risk of excluding factors that are actually associated with patient risk. In some embodiments, data preparation at block 860 may include modifying or removing coding data, treatment data, etc. At block 862, the system may extract features from the training data and at block 864, the system may train a model using the training data to generate a model 866. At block 868, in some embodiments, the system may evaluate the model to determine whether it passes one or more criteria. In some embodiments, at decision point 870, if the model fails, the system may perform additional training. In some embodiments, if the model passes at decision point 870, the system may make available the model 872 after completion of training.

[0184] In some embodiments, the trained model 872 can be used to evaluate a particular user. User data 874 may be associated with a particular user for whom the output of the trained model 872 is desired. At block 876, the system may prepare data, for example, as described above in connection with stored training data. In some embodiments, at block 878, the system may extract features from the prepared user data. In some embodiments, the system may be configured to provide the extracted features to the learned model 872 to generate results 880. The results 880 can be used, for example, to determine a risk level associated with the user and / or to determine one or more risk subscores for the user.

[0185] In some embodiments, the user data 874, the results 880, and other information about the user (e.g., information about the user's outcomes after receiving or not receiving treatment for a plaque-based disease) can be used to train the model. At block 882, in some embodiments, the system may have the user prepare the user data 874 and the results 880 for use in training. In some embodiments, preparing the data may include, for example, anonymizing the data. For example, in some embodiments, any information regarding the patient's name, social security number, or other information that may be personally identifiable may be removed. In some embodiments, the system may partially anonymize the user data 874 by changing the user's date of birth, for example, retaining only the year the user was born (as age is often an important factor in evaluating a medical history) or the year and month the user was born. In some embodiments, the system may store the prepared data in training data store 858. In some embodiments, the prepared data may additionally or alternatively be stored in another database or data store. In some embodiments, the system may retrain the model periodically, continuously, or whenever an operator indicates to the system that the model needs to be retrained. Thus, in some embodiments, the trained model 872 can evolve over time, such that risk assessment can improve over time, for example, as the model is trained with additional data.

[0186] [Plaque morphology / characteristics analysis] As discussed herein, disclosed herein are systems, methods, and devices for non-invasive, image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein relate to the analysis of one or more regions of plaque, such as coronary artery plaque, based on one or more distance, volume, shape, morphology, depth of embedding, and / or axis measurements. For example, in some embodiments, the systems, devices, and methods described herein relate to plaque analysis based on one or more of the following: the distance between the plaque and the vessel wall, the distance between the plaque and the lumen wall, the length along the anterior-posterior axis, the length along the lateral axis, the volume of low-density non-calcified plaque, the volume of total plaque, the ratio(s) between the volume of low-density non-calcified plaque and the volume of total plaque, the depth of embedding of low-density non-calcified plaque, and / or the like. In some embodiments, the systems, devices, and methods described herein are configured to determine the risk of coronary artery disease (CAD), such as myocardial infarction (MI), based on one or more plaque analyses described herein. In some embodiments, the systems, devices, and methods described herein are configured to generate suggested treatments and / or visual representations based on the determined CAD risk and / or one or more plaque analyses described herein.

[0187] More specifically, in some embodiments, systems, methods, and devices may be configured to analyze medical images to perform one or more analyses of plaques and / or plaque types, such as low-density non-calcified plaque, calcified plaque, and non-calcified plaque. In particular, in some embodiments, low-density non-calcified plaque may be noted due to the higher risk generally associated with low-density non-calcified plaque. For example, low-density non-calcified plaque may have a higher risk of potential rupture compared to other types of plaque, such as normal non-calcified plaque or calcified plaque. Plaque rupture can potentially clog or block a blood vessel, causing a heart attack or myocardial infarction. As such, it may be advantageous to analyze one or more characteristics of low-density non-calcified plaque, non-calcified plaque, and / or calcified plaque, which may correspond to a high or low risk of CAD and / or plaque stability or instability. In some embodiments, systems, devices, and methods are configured to analyze medical images, such as CT or CCTA images, to derive one or more features, measurements, and / or characterizations of plaque, such as low-density non-calcified plaque, non-calcified plaque, and / or calcified plaque, and use the same to facilitate or generate an assessment of CAD risk and / or plaque stability or instability. Thus, in some embodiments, the systems, devices, and methods can provide an efficient and / or non-invasive method of assessing CAD and / or plaque risk.

[0188] FIG. 9A shows examples of sample measurements, dimensions, and / or analyses related to plaque analysis and risk assessment. For example, FIG. 9A(a) shows an example of a low-density non-calcified (LDNC) aggregate associated with a single coronary artery lesion. FIG. 9A(b) shows an example of plaque distance relative to the lumen wall and vessel wall. FIG. 9A(c) shows an example of plaque dimension axis measurements along the major and minor axes. FIG. 9A(d) shows an example of the degree of LDNC plaque embedding (DELP). FIG. 9A(e) shows examples of four different shapes of plaque or plaque regions, including crescent-shaped, roughly round, leaf-shaped, and bean-shaped plaques.

[0189] As shown in FIG. 9A , in some embodiments, a system may be configured to analyze medical images of a blood vessel, artery, and / or portion thereof, such as a coronary artery lesion. In some embodiments, a particular blood vessel, artery, and / or lesion may include one or more regions of plaque. “Density” and “radiodensity” generally relate to the opacity (i.e., the relative inability of radiation to pass through a particular material) of a particular material depicted in a medical image; the higher the density / radiodensity, the more opaque the material. In some embodiments, based on the absolute or material density and / or relative density and / or radiodensity of features in one or more medical images, the system may be configured to characterize a region of plaque into one or more subtypes of plaque. For example, in some embodiments, the system may be configured to characterize a region of plaque as either low-density non-calcified plaque, non-calcified plaque, or calcified plaque. In some embodiments, the system may be configured to characterize a region of plaque as one or more of low-density non-calcified plaque, non-calcified plaque, or calcified plaque. In some embodiments, calcified plaque may correspond to plaques having the highest density range, low-density non-calcified plaque may correspond to plaques having the lowest density range, and non-calcified plaque may correspond to plaques having a density range between calcified and low-density non-calcified plaques. For example, in some embodiments, the system may be configured to characterize a particular region of plaque as low-density non-calcified plaque if the radiometric density of image pixels or voxels corresponding to that region of plaque is between about -189 and about 30 Hounsfield Units (HU). In some embodiments, the system may be configured to characterize a particular region of plaque as non-calcified plaque if the radiometric intensity of image pixels or voxels corresponding to that region of plaque is between about 31 and about 350 HU.In some embodiments, the system may be configured to characterize a particular region of plaque as calcified plaque if the radiation intensity of the image pixels or voxels corresponding to that region of plaque is between about 351 and about 2500 HU.In some embodiments, the lower and / or upper Hounsfield unit boundary threshold for determining whether plaque corresponds to one or more of low density non-calcified plaque, non-calcified plaque, and / or calcified plaque can be about -1000 HU, about -900 HU, about -800 HU, about -700 HU, about -600 HU, about -500 HU, about -400 HU, about -300 HU, about -200 HU, about -190 HU, about -180 HU, about -170 HU, about -160 HU, about -150 HU, about -140 HU, about -130 HU, about -120 HU , about -110HU, about -100HU, about -90HU, about -80HU, about -70HU, about -60HU, about -50HU, about -40HU, about -30HU, about -20HU, about -10HU, about 0HU, about 10HU, about 20HU, about 30HU, about 40HU, about 50HU, about 60HU, about 7 0HU, about 80HU, about 90HU, about 100HU, about 110HU, about 120HU, about 130HU, about 140HU, about 150HU, about 160HU, about 170HU, about 180HU, about 190HU, about 200HU, about 210HU, about 220HU, about 230HU, about 240HU, about 25 0HU, about 260HU, about 270HU, about 280HU, about 290HU, about 300HU, about 310HU, about 320HU, about 330HU, about 340HU, about 350HU, about 360HU, about 370HU, about 380HU, about 390HU, about 400HU, about 410HU, about 420HU, 430HU, 440HU, 450HU, 460HU, 470HU, 480HU, 490HU, 500HU, 510HU, 520HU, 530HU, 540HU, 550HU, 560HU, 570HU, 580HU, 590HU, 600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about 2000 HU, about 2100 HU, about 2200 HU, about 2300 HU, about 2400 HU, about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900 HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400 HU, about 3500 HU, and / or about 4000 HU.

[0190] In some embodiments, the system may be configured to characterize one or more regions of plaque and generate a representation of the same on a normalized or multiplanar view of the vessel, as illustrated, for example, in FIG. 9A(a). In the example illustrated in FIG. 9A(a), this particular coronary artery lesion consists of a region of non-calcified plaque 901 with one or more regions of calcified plaque 903 and two regions of low-density non-calcified plaque 905. In some embodiments, the systems, methods, and devices may be configured to perform one or more analyses on one or more regions of plaque that may be factors and / or indicators of plaque stability and / or risk of CAD or MI. For example, in some embodiments, the systems, methods, and devices may be configured to determine the distance from the region of low-density non-calcified plaque to the vessel wall and / or lumen wall. In one example, the distance can be the shortest distance from the low-density non-calcified plaque to the vessel wall and / or lumen wall, or in another example, the distance can be the average distance from multiple points on the low-density non-calcified plaque to the vessel wall and / or lumen wall. This is further explained with reference to FIG. 9A(b) below. In some embodiments, and as further described with reference to FIG. 9A(c), systems, methods, and devices can be configured to determine one or more dimensions, such as the major axis and / or minor axis, of a region of low-density non-calcified plaque. In some embodiments, and as further described with reference to FIG. 9A(d), systems, methods, and devices can be configured to determine the degree of embedding of the low-density non-calcified plaque. In some embodiments, and as further described with reference to FIG. 9A(e), systems, methods, and devices can be configured to determine the shape or morphology of the low-density non-calcified plaque. In some embodiments, systems, methods, and devices can be configured to determine the volume or size of the low-density non-calcified plaque, total plaque, and / or a proportion thereof.In some embodiments, systems, methods, and devices may be configured to consider one or some or all of the foregoing analyses when assessing plaque stability or instability and / or risk of CAD or MI on a patient or subject basis, vessel basis, lesion basis, etc. In some embodiments, systems, methods, and devices may be configured to generate a weighted measure of one or some or all of the foregoing analyses when assessing plaque stability or instability and / or risk of CAD or MI on a patient or subject basis, vessel basis, lesion basis, and / or the like. In some embodiments, systems, methods, and devices may be configured to utilize one or more artificial intelligence (AI) and / or machine learning (ML) algorithms when performing any one or more of the analyses described herein.

[0191] In particular, in some embodiments, the systems, methods, and devices may be configured to measure and / or determine the distance from an area of ​​low-density, non-calcified plaque to the lumen and / or vessel wall. In some embodiments, the distance from an area of ​​low-density, non-calcified plaque to the lumen and / or vessel wall may indicate the stability of the low-density, non-calcified plaque and, therefore, may be considered a factor in determining the risk of CAD resulting from that low-density, non-calcified plaque. For example, in some embodiments, low-density, non-calcified plaque closer to the lumen wall may be more likely to rupture and cause an MI.

[0192] In particular, in some embodiments, the system may be configured to determine the shortest distance between a point within the boundary of a region of low-density non-calcified plaque and the vessel wall and / or lumen wall. In some embodiments, the system may be configured to determine the distance between the low-density non-calcified plaque and the vessel wall and / or lumen wall based on two-dimensional slice images and / or three-dimensional renderings.

[0193] For example, as shown in the example of FIG. 9A(b), in some embodiments, the system may be configured to analyze a two-dimensional slice that renders the maximum cross-sectional area of ​​the low-density non-calcified plaque. In some embodiments, the two-dimensional slice may be perpendicular to the anterior-posterior axis of the normalized multiplanar view of the vessel. In some embodiments, the system may be configured to automatically and / or semi-automatically determine the distance from the low-density non-calcified plaque to the vessel wall and / or lumen wall. In some embodiments, the system may be configured to assist a user in determining the distance from the low-density non-calcified plaque to the vessel and / or lumen wall. For example, in some embodiments, the system may be configured to generate a graphical user interface that allows a user to click two points on the image, after which the system may automatically determine the distance between the two points. In some embodiments, the system may be configured to utilize digital calipers to determine the distance between the low-density non-calcified plaque and the vessel wall or lumen wall.

[0194] FIG. 9A(b) shows distance 907 from low-density non-calcified plaque to the lumen wall and distance 909 from low-density non-calcified plaque to the vessel wall. In some embodiments, the system can be configured to average multiple distances from one or more lumens and / or vessel walls to determine the distance from the region of plaque to the lumen and / or vessel wall. In some embodiments, the boundary of the region of low-density non-calcified plaque can be determined at the end of the plaque density gradient that partially includes low-density non-calcified plaque that is farthest from the center of the region of low-density non-calcified plaque. In some embodiments, the boundary of the region of low-density non-calcified plaque can be determined at the end of the plaque density gradient that is partially composed of low-density non-calcified plaque that is closest to the center of the region of low-density non-calcified plaque. In some embodiments, the boundary of the region of low-density non-calcified plaque can be determined within the plaque density gradient that is partially composed of low-density non-calcified plaque between the farthest and nearest points from the center of the region of low-density non-calcified plaque. In some embodiments, a shorter distance between the border of the region of low-density non-calcified plaque and the lumen and / or vessel wall indicates a higher risk of CAD or MI, while a longer distance between the border of the region of low-density non-calcified plaque and the lumen and / or vessel wall indicates a lower risk of CAD or MI. For example, in some embodiments, the system may be configured to characterize CAD or MI risk as high or low, and / or plaque risk as high or low, when a distance, such as the shortest distance, between the boundary of the region of low-density non-calcified plaque and the lumen and / or vessel wall is about 0.0 mm, about 0.1 mm, about 0.2 mm, about 0.3 mm, about 0.4 mm, about 0.5 mm, about 0.6 mm, about 0.7 mm, about 0.8 mm, about 0.9 mm, about 1.0 mm, about 1.1 mm, about 1.2 mm, about 1.3 mm, about 1.4 mm, about 1.5 mm, about 1.6 mm, about 1.7 mm, about 1.8 mm, about 1.9 mm, about 2.0 mm, and / or any value therebetween, greater than, or less than these.

[0195] In some embodiments, the systems, methods, and devices are configured to determine one or more dimensions of low-density non-calcified plaque, as shown in FIG. 9A(c). For example, in some embodiments, the system may be configured to measure and / or determine the length of the long and / or short axes of the region of low-density non-calcified plaque. In some embodiments, the system may be configured to automatically or semi-automatically measure and / or determine the length of the long and / or short axes of the region of low-density non-calcified plaque, for example, using image processing algorithms. In some embodiments, the system may be configured to measure and / or determine the length of the long and / or short axes of the region of low-density non-calcified plaque based at least in part on user input, for example, using digital calipers. In some embodiments, the system may be configured to measure and / or determine the length of the long and / or short axes of the region of low-density non-calcified plaque based on three-dimensional analysis and / or based on one or more two-dimensional images.

[0196] For example, as shown in the example of FIG. 9A(c), in some embodiments, the system is configured to capture longitudinal slice images of the region of plaque and measure the long and / or short axes of the low-density non-calcified plaque. In some embodiments, the longitudinal slices may be captured parallel to the anterior-posterior axis of a multiplanar or normalized multiplanar view of the vessel. In some embodiments, the longitudinal slices may be captured to show the longest possible anterior-posterior axis of the low-density non-calcified plaque. In some embodiments, the system is configured to capture transverse slice images of the region of plaque and measure the long and / or short axes of the low-density non-calcified plaque. In some embodiments, the transverse slices may be captured perpendicular or orthogonal to the anterior-posterior axis of a multiplanar or normalized multiplanar view of the vessel and / or the longitudinal slices. In some embodiments, the longitudinal slices may be captured to show the largest possible cross-sectional area of ​​the low-density non-calcified plaque. In some embodiments, the system may be configured to determine measurements of one or more orthogonal axes of the low-density non-calcified plaque.

[0197] In some embodiments, such measurements of one or more axes of a region of low-density non-calcified plaque may be utilized by the system for further characterization and / or analysis. For example, in some embodiments, the measurement(s) of one or more axes of a region of low-density non-calcified plaque may be utilized, directly or indirectly, as a factor in assessing plaque stability and / or a subject's risk of CAD or MI. In some embodiments, the system may be configured to utilize one or more measurements of one or more axes of a region of low-density non-calcified plaque to determine and / or estimate the size, volume, and / or shape of the low-density non-calcified plaque, which may in turn be correlated to an assessment of plaque stability and / or CAD or MI risk. In some embodiments, the system may be configured to directly correlate one or more measurements of one or more axes of a region of low-density non-calcified plaque to an assessment of plaque stability and / or CAD or MI risk. In some embodiments, a region of low-density non-calcified plaque with a larger area, length, diameter, or volume may correspond to a higher risk of CAD or MI. In some embodiments, regions of low-density non-calcified plaque that are smaller in area, length, diameter, or volume may correspond to a lower risk of CAD or MI. For example, in some embodiments, the system may be configured to characterize a region of low-density non-calcified plaque as having a high or low risk of CAD or MI and / or a high or low risk of plaque when the region of low-density non-calcified plaque has a length or diameter of about 0.0 mm, about 0.0 mm, about 0.1 mm, about 0.2 mm, about 0.3 mm, about 0.4 mm, about 0.5 mm, about 0.6 mm, about 0.7 mm, about 0.8 mm, about 0.9 mm, about 1.0 mm, about 1.1 mm, about 1.2 mm, about 1.3 mm, about 1.4 mm, about 1.5 mm, about 1.6 mm, about 1.7 mm, about 1.8 mm, about 1.9 mm, about 2.0 mm, and / or any value therebetween, greater than, or less than these values, and / or the corresponding area and / or volume. 2, approximately 0.01 mm 2 , approximately 0.04 mm 2 , approximately 0.09 mm 2 , about 0.16mm 2 , about 0.25mm 2 , approximately 0.36 mm 2 , approx. 0.49 mm 2 , approx. 0.64 mm 2 , approx. 0.81 mm 2 , approx. 1.0 mm 2 , approx. 1.21 mm 2 , approx. 1.44 mm 2 , approx. 1.69 mm 2 , approx. 1.96 mm 2 , approx. 2.25 mm 2 , approx. 2.56 mm 2 , approx. 2.89 mm 2 , approximately 3.24 mm 2 , approximately 3.61 mm 2 , approximately 4.0 mm 2 , and / or any value therebetween, above, or below these values, and / or having a corresponding length, diameter, and / or volume, the system may be configured to characterize a region of low density non-calcified plaque as being at high or low risk of CAD or MI and / or at high or low risk of plaque. For example, in some embodiments, the system may be configured to characterize a region of low density non-calcified plaque as being at high or low risk of CAD or MI when the volume of the region of low density non-calcified plaque is about 0.0 mm 3 , approximately 0.000001 mm 3 , approximately 0.000008 mm 3 , approximately 0.000027 mm 3 , approximately 0.000064 mm 3 , approximately 0.000125 mm 3 , approximately 0.0.000216 mm 3 , approx. 0.000343 mm 3 , approx. 0.000343 mm 3 , approx. 0.000729 mm 3 , approx. 1.0 mm 3 , approx. 1.331 mm 3 , approx. 1.728 mm 3 , approx. 2.197 mm 3 , approx. 2.744 mm3 , approx. 3.375 mm 3 , approx. 4.096 mm 3 , approximately 4.913 mm 3 , approximately 4.913 mm 3 , approximately 6.859 mm 3 , approximately 8.0 mm 3 , and / or any number between, above, or below these, and / or having a corresponding length, diameter, and / or volume may be configured to characterize a high or low risk of CAD or MI and / or a high or low risk of plaque.

[0198] In some embodiments, systems, methods, and devices can be configured to determine the degree of embedding of low-density non-calcified plaque, for example, by normal non-calcified plaque and / or calcified plaque. In some embodiments, the degree of embedding of low-density non-calcified plaque can be a factor and / or indicator of plaque stability and / or risk of CAD or MI, since the more embedded a low-density non-calcified plaque is, the more likely it is to rupture and / or to have or develop a thin fibrous cap. In some embodiments, systems can be configured to determine the degree of embedding of low-density non-calcified plaque based on the degree of encapsulation of the low-density non-calcified plaque by non-calcified or calcified plaque. In some embodiments, the degree of embedding of low-density non-calcified plaque is determined by measuring how much of the boundary of the low-density non-calcified plaque region is in contact with other types of plaque, in relation to how much of the boundary of the low-density non-calcified plaque region is in contact with the vessel wall or lumen. In some embodiments, the system may be configured to measure and / or determine the degree of embedding of an area of ​​low-density non-calcified plaque based on a three-dimensional analysis and / or based on one or more two-dimensional images.

[0199] For example, as shown in the example of FIG. 9A(d), in some embodiments, the system is configured to capture longitudinal slice images of the region of plaque and measure the degree of embedment of the low-density non-calcified plaque. In some embodiments, the longitudinal slices can be captured parallel to the anterior-posterior axis of the multiplanar view or normalized multiplanar view of the vessel. In some embodiments, the system can be configured to capture latitudinal slice images of the region of plaque and measure the degree of embedment of the low-density non-calcified plaque. In some embodiments, the latitudinal slices can be captured perpendicular or orthogonal to the anterior-posterior axis of the multiplanar view or normalized multiplanar view of the vessel and / or the latitudinal slices. In one example, the degree of embedment is 180 degrees when half of the boundary of the region of low-density non-calcified plaque touches the vessel wall and the other half of the boundary of the region of low-density non-calcified plaque touches the calcified plaque. In another example, when three-quarters of the boundary of the region of low-density non-calcified plaque is in contact with the vessel wall and another quarter of the boundary of the region of low-density non-calcified plaque is in contact with calcified plaque, the degree of burial is 90 degrees. In another example, when one-quarter of the boundary of the region of low-density non-calcified plaque is in contact with the vessel wall and another three-quarters of the boundary of the region of low-density non-calcified plaque is in contact with calcified plaque, the degree of burial is 270 degrees. In another example, when the entire boundary of the region of low-density non-calcified plaque is in contact with calcified plaque, the degree of burial is 360 degrees. In some embodiments, the system may be configured to determine the degree of burial of low-density non-calcified plaque as approximately 0°, approximately 30°, approximately 60°, approximately 90°, approximately 120°, approximately 150°, approximately 180°, approximately 210°, approximately 240°, approximately 270°, approximately 300°, approximately 330°, approximately 360°, and / or one or more between ranges defined by two of these values. In the example shown in FIG. 9A(d), in some embodiments, the degree of burial of low-density non-calcified plaque is determined to be approximately 90°, approximately 180°, approximately 270°, and approximately 360°, respectively. In some embodiments, a degree of burial of low-density non-calcified plaque of approximately 270° or greater may be considered high-risk plaque.In some embodiments, the system may be configured to automatically or semi-automatically determine the degree of coverage of a region of low-density non-calcified plaque, for example, using image processing algorithms. In some embodiments, the system may be configured to determine the degree of coverage of a region of low-density non-calcified plaque based at least in part on user input. In some embodiments, the system may be configured to visually overlay a protractor on the medical image to assist in and / or indicate the degree of coverage of a region of low-density non-calcified plaque.

[0200] In some embodiments, systems, methods, and devices may be configured to determine the shape or morphology of a region of low-density non-calcified plaque. In some embodiments, the system may be configured to determine and / or characterize the shape or morphology of low-density non-calcified plaque as one or more of crescent, round, leaf, or bean shape. In some embodiments, the shape or morphology of low-density non-calcified plaque may be a factor and / or indicator of plaque stability and / or CAD risk. In some embodiments, the crescent shape may have a length greater than its width. In some embodiments, the crescent shape may be a thin, curved shape. In some embodiments, the crescent shape may be a narrow shape. In some embodiments, the crescent shape may resemble a curved or non-curved line. For example, in some embodiments, crescent-shaped low-density non-calcified plaque may be associated with a lower risk of CAD or MI. In some embodiments, this may be because what appears to be a crescent-shaped plaque by image analysis may correspond to perivascular fat rather than true low-density, non-calcified plaque, especially when found, for example, around or near or adjacent to the vascular wall. In some embodiments, the round shapes can have similar or approximately equal lengths and widths. In some embodiments, the round shapes can have similar or approximately consistent diameters. In some embodiments, the round shapes can resemble circles or ellipses. In some embodiments, the bean-shaped shapes can be composed of two or three interconnected round and / or crescent shapes. In some embodiments, the bean-shaped shapes can be wider shapes composed of interconnected round and / or crescent shapes. In some embodiments, the bean-shaped shapes can be distorted round shapes. In some embodiments, the bean-shaped shapes can have inconsistent diameters. In some embodiments, round or bean-shaped low-density, non-calcified plaques may be associated with a higher risk of CAD or MI. In some embodiments, the leaflets can be composed of four or more interconnected round and / or crescent shapes.In some embodiments, lobes may be narrow shapes composed of interconnected round and / or crescent shapes. In some embodiments, lobes may be distorted crescent shapes. In some embodiments, lobe-shaped low-density non-calcified plaques may be associated with a higher risk of CAD compared to crescent-shaped plaques, but may be associated with a lower risk of CAD compared to bean-shaped or round-shaped low-density non-calcified plaques. In some embodiments, low-density non-calcified plaques may be classified as lobe-shaped or bean-shaped based on the number of lobes. For example, in some embodiments, low-density non-calcified plaques with more lobes may be classified as lobe-shaped, while low-density non-calcified plaques with fewer lobes may be classified as bean-shaped. Figure 9A(e) shows examples of crescent-shaped, round-shaped, lobe-shaped, and / or bean-shaped regions of low-density non-calcified plaques.

[0201] In some embodiments, the system may be configured to automatically or semi-automatically classify the shape or morphology of a region of low-density non-calcified plaque, for example, using an image processing algorithm. In some embodiments, the system may be configured to classify the shape or morphology of a region of low-density non-calcified plaque based, at least in part, on user input. In some embodiments, the system may be configured to classify the shape or morphology of a region of low-density non-calcified plaque based, at least in part, on one or more measurements of one or more axes of the low-density non-calcified plaque. For example, in some embodiments, the system may be configured to compare the long, long-lateral, and / or short-lateral axes of a region of low-density non-calcified plaque when determining the shape of the low-density non-calcified plaque. In some embodiments, the system may be configured to take one or more standard deviations of the long, long-lateral, and / or short-lateral axes and classify the low-density non-calcified plaque as round if the standard deviation is below a certain predetermined threshold. In some embodiments, crescent-shaped, leaf-shaped, and / or bean-shaped low-density non-calcified plaque may be associated with a higher standard deviation between the axial measurements.

[0202] In some embodiments, systems, devices, and methods may be configured to determine the volume or size of total plaque, low-density non-calcified plaque, non-calcified plaque, and / or calcified plaque, and / or their ratios. In some embodiments, the volume or size of total plaque, low-density non-calcified plaque, non-calcified plaque, and / or calcified plaque may be a factor and / or indicator for determining plaque stability and / or assessing the risk of CAD or MI. For example, in some embodiments, a larger volume of total plaque, low-density non-calcified plaque, and / or non-calcified plaque may indicate high-risk plaque and / or a higher risk of CAD. In some embodiments, a higher ratio of low-density non-calcified plaque to total plaque may indicate high-risk plaque and / or a higher CAD risk.

[0203] In some embodiments, the system may be configured to automatically or semi-automatically determine the size or volume of the region of low-density non-calcified plaque, for example, using image processing algorithms. In some embodiments, the system may be configured to determine the size or volume of the region of low-density non-calcified plaque based at least in part on user input. In some embodiments, the system may be configured to determine the size or volume of the region of low-density non-calcified plaque based at least in part on one or more measurements of one or more axes of the low-density non-calcified plaque.

[0204] [Sample study on plaque morphology / characteristics analysis] As discussed herein, in some embodiments, systems, methods, and devices are configured to analyze one or more plaque morphology and / or other features as indicators and / or variables for determining risk of CAD and / or MI. For example, in some embodiments, systems, methods, and devices may be configured to analyze medical images to derive one or more plaque parameters and / or vascular parameters, such as total plaque volume, low-density non-calcified plaque volume, non-calcified plaque volume, calcified plaque volume, minimum lumen diameter, maximum remodeling index, maximum lesion length, and / or the like. In some embodiments, systems, methods, and devices can be used to indicate and / or derive a correlation between one or more such features and risk of CAD and / or MI.

[0205] Figure 9B shows an example of the morphological characteristics of high-risk plaques per lesion obtained from the sample study. As shown, Figure 9B summarizes the frequency of embedded low-density non-calcified plaque (DELP) and low-density non-calcified (LDNC) deposit shapes in non-culprit and culprit lesions ((-) culprit and (+) culprit, respectively). The illustrated sample study included 122 patients whose culprit lesions were determined at the time of an acute coronary syndrome (ACS) event.

[0206] Figure 9C shows an example of atherosclerosis profile by patient stratified by patients with and without acute coronary syndrome events from a sample study. In Figure 9C, atherosclerosis profile by patient is shown stratified by patients without and with acute coronary syndrome events ((-)ACS and (+)ACS, respectively).

[0207] Figure 9D shows an example of adjusted hazard ratios for the impact of high-risk plaque morphological characteristics on precursor lesions of acute coronary syndromes. In the example study summarized in Figure 9D, 122 patients with 965 coronary artery lesions were evaluated. In this study, all patients underwent culprit lesion determination at the time of acute coronary syndrome (ACS) onset. Figure 9D shows the hazard ratios for the degree of embeddedness of low-density non-calcified plaque (DELP) and the shape of low-density non-calcified plaque (crescent, bean, round, and leaf) for each lesion in a forest plot. In this study, high-risk plaque morphology (HRP morphology) was defined as lesions with round or bean-shaped low-density non-calcified plaque deposits and a DELP greater than 180°. Furthermore, Figure 9D also shows lesion characteristics such as low-density non-calcified plaque ((+) LDNC Plaque), positive remodeling ((+) Positive Remodeling), and two-characteristic plaque ((+) 2FPP) for comparison. A (+) 2FPP indicates the presence of both positive remodeling and any amount of low-density noncalcified plaque for a given lesion. In this study, hazard ratios were adjusted for the occurrence of angina and revascularization. Figure 9D also shows the proportion of non-culprit lesions ((-) culprit lesions) and culprit lesions ((+) culprit lesions) for each variable described in the forest plot.

[0208] Figure 9E shows an example of Kaplan-Meier curves of acute coronary syndrome incidence in patients with and without high-risk plaque morphology lesions obtained from a sample study. In Figure 9E, patients without at least one high-risk plaque morphology lesion are represented by (-)HRP morphology, and patients with at least one high-risk plaque morphology lesion are represented by (+)HRP morphology.

[0209] [Plaque morphology / characterization - additional details] FIG. 9F is a flowchart illustrating exemplary embodiment(s) of a system, device, and method for non-invasive image-based plaque analysis and risk assessment. As shown in FIG. 9F, in some embodiments, the system may be configured to access and / or modify one or more medical images at block 902. In some embodiments, the medical images may include one or more arteries, such as the subject's coronary arteries, carotid arteries, and / or other arteries. In some embodiments, the medical images may be stored in a medical image database 904. In some embodiments, the medical image database 904 may be accessed locally by the system and / or may be remotely located and accessed via a network connection. The medical images may include images obtained using one or more modalities, such as CT, dual-energy computed tomography (DECT), spectral CT, photon-counting CT, X-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), magnetic resonance (MR) imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single-photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS). In some embodiments, the medical images include one or more of contrast-enhanced CT images, non-contrast-enhanced CT images, magnetic resonance imaging, and / or images obtained using any of the aforementioned modalities.

[0210] In some embodiments, the system may be configured to automatically and / or dynamically perform one or more analyses of the medical images as discussed herein. For example, in some embodiments, at block 906, the system may be configured to identify one or more blood vessels, such as one or more arteries. The one or more arteries may include, among others, coronary arteries, carotid arteries, aorta, renal arteries, arteries of the lower limbs, arteries of the upper limbs, and / or cerebral arteries. In some embodiments, the system may be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically identify one or more arteries or coronary arteries using image processing. For example, in some embodiments, one or more AI and / or ML algorithms may be trained using a convolutional neural network (CNN) on a set of medical images in which arteries or coronary arteries have been identified, thereby enabling the AI ​​and / or ML algorithms to automatically identify the arteries or coronary arteries directly from the medical images. In some embodiments, the arteries or coronary arteries are identified by size and / or location.

[0211] In some embodiments, at block 908, the system may be configured to identify one or more regions of plaque within the medical image. In some embodiments, the system may be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically identify one or more regions of plaque using image processing. For example, in some embodiments, one or more AI and / or ML algorithms may be trained using a convolutional neural network (CNN) on a set of (digital) medical images in which regions of plaque have been identified, thereby allowing the AI ​​and / or ML algorithms to automatically identify regions of plaque directly from the medical images. In some embodiments, the system is configured to identify the vessel wall and the lumen wall and classify everything between the vessel wall and the lumen wall as plaque.

[0212] In some embodiments, at block 910, the system may be configured to analyze and / or characterize one or more regions of plaque based on density. For example, in some embodiments, the system may be configured to analyze and / or characterize one or more regions of plaque based on absolute density and / or relative density and / or radiometric density. In some embodiments, the system may be configured to classify a region of plaque as one of low-density non-calcified plaque, non-calcified plaque, and calcified plaque using any one or more processes and / or features described herein.

[0213] In some embodiments, at block 912, the system may be configured to analyze and / or characterize one or more regions of plaque based on one or more distances. For example, as described herein, in some embodiments, the system may be configured to determine the distance between low-density non-calcified plaque and the lumen wall and / or vessel wall. In some embodiments, the proximity of low-density non-calcified plaque to the lumen wall may indicate high-risk plaque and / or CAD. Conversely, in some embodiments, the location of low-density non-calcified plaque farther from the lumen wall may indicate a lower risk. In some embodiments, the system may be configured to utilize one or more predetermined thresholds in determining risk factors associated with the proximity of low-density non-calcified plaque to the vessel wall and / or lumen wall. In some embodiments, the system may be configured to utilize one or more image processing algorithms to automatically and / or dynamically determine one or more distances to and / or from one or more regions of plaque.

[0214] In some embodiments, at block 914, the system may be configured to analyze and / or characterize one or more regions of the low-density non-calcified plaque based on the morphology or shape and / or one or more axial measurements of the plaque. As described herein, in some embodiments, the system may be configured to determine the length of one or more axes of the low-density non-calcified plaque, such as the long axis of a longitudinal section and / or the long axis and / or short axis of a transverse section of the low-density non-calcified plaque. In some embodiments, the system may be configured to utilize one or more axial measurements to determine the morphology and / or shape of the low-density non-calcified plaque. In some embodiments, the system may be configured to utilize one or more image processing algorithms to automatically and / or dynamically determine one or more axial measurements of one or more regions of the plaque.

[0215] In some embodiments, the system can be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically classify the shape of one or more regions of plaque using image processing. For example, in some embodiments, one or more AI and / or ML algorithms can be trained using a convolutional neural network (CNN) on a set of medical images in which the shapes of regions of plaque have been identified, thereby allowing the AI ​​and / or ML algorithms to automatically identify the shape or morphology of the regions of plaque directly from the medical images. In some embodiments, the system can be configured to classify the shape or morphology of the regions of plaque as one or more of crescent-shaped, leaf-shaped, round, or bean-shaped. In some embodiments, round-shaped and / or bean-shaped plaques can be associated with a high risk, while crescent-shaped and / or leaf-shaped plaques can be associated with a low risk of CAD.

[0216] In some embodiments, at block 916, the system may be configured to analyze and / or characterize one or more regions of plaque based on one or more size and / or volume measurements. For example, in some embodiments, the system may be configured to determine plaque size and / or volume based at least in part on one or more axial measurements described herein. In some embodiments, the system may be configured to determine the size and / or volume of a region of plaque directly from analysis of a three-dimensional image scan. In some embodiments, the system may be configured to determine the size and / or volume of total plaque, low-density non-calcified plaque, non-calcified plaque, calcified plaque, and / or the ratio between any two of the foregoing volumes or sizes. In some embodiments, a high total plaque volume and / or a high low-density non-calcified plaque and / or non-calcified plaque volume may be associated with a high risk of CAD. In some embodiments, a high ratio of low-density non-calcified plaque volume to total plaque volume and / or a high ratio of non-calcified plaque volume to total plaque volume may be associated with a high risk of CAD. In some embodiments, a high calcified plaque volume and / or a high ratio of calcified plaque volume to total plaque volume may be associated with a low risk of CAD. In some embodiments, the system may be configured to utilize one or more predetermined thresholds for determining CAD risk based on plaque volume, size, or one or more ratios thereof. In some embodiments, the system may be configured to utilize one or more image processing algorithms to automatically and / or dynamically determine the size and / or volume of one or more regions of plaque.

[0217] In some embodiments, at block 918, the system may be configured to analyze and / or characterize plaque based on its degree of embedment. For example, in some embodiments, the system may be configured to determine the extent to which low-density non-calcified plaque is embedded or surrounded by non-calcified plaque or calcified plaque. In some embodiments, the system may be configured to analyze the degree of embedment of low-density non-calcified plaque based on whether the low-density non-calcified plaque is surrounded by other types of plaque. In some embodiments, a higher degree of embedment of low-density non-calcified plaque may indicate a higher risk of CAD. For example, in some embodiments, low-density non-calcified plaque surrounded by 270 degrees or more of non-calcified plaque may be associated with a higher risk of CAD. In some embodiments, the system may be configured to utilize one or more image processing algorithms to automatically and / or dynamically determine the degree of embedment of one or more regions of plaque.

[0218] In some embodiments, at block 920, the system may be configured to determine the risk of CAD or MI based on one or more plaque analyses described herein, e.g., in connection with one or more of blocks 902-918. In some embodiments, the system may be configured to utilize some or all of the plaque analysis results. In some embodiments, the system may be configured to generate a weighted scale of some or all of the plaque analyses described herein when determining the risk of CAD. In some embodiments, the system may be configured to reference one or more reference values ​​of one or more plaque analyses when determining the risk of CAD. For example, in some embodiments, the one or more reference values ​​may be comprised of one or more values ​​obtained from populations with different CAD risk statuses, and the one or more values ​​may be comprised of one or more of the following: distance to and / or distance from low-density non-calcified plaque, one or more axial measurements, morphological classification, size and / or volume, and / or embedding of low-density non-calcified plaque. In some embodiments, the one or more reference values ​​may be stored in a reference value database 922, which may be locally accessible by the system and / or may be remotely located and accessed via a network connection.

[0219] In some embodiments, at block 924, the system may be configured to generate a visual display of the analysis results, the determined CAD risk, and / or the subject's proposed treatment. In some embodiments, the analysis results may be displayed on a vessel, lesion, and / or subject basis. In some embodiments, the suggested treatment may include, for example, a medical treatment such as a statin, an interventional treatment such as stent implantation, and / or a lifestyle treatment such as exercise or diet. In some embodiments, in determining the risk or status of cardiovascular disease or health and / or treatment, the system may access a plaque risk / treatment database 926, which may be accessed locally by the system and / or may be remotely located and accessed via a network connection. In some embodiments, the plaque risk / treatment database 926 may include reference points or data relating one or more treatments to the determined cardiovascular disease risk or status based on one or more baseline plaque analysis values.

[0220] In some embodiments, the system may be configured to repeat one or more of the processes described in connection with blocks 902-926, for example, for one or more other vessels, segments, regions of plaque, different subjects, and / or the same subject at different times. Thus, in some embodiments, the system may provide longitudinal disease tracking and / or personalized treatment for a subject.

[0221] [Computer System] In some embodiments, the systems, processes, and methods described herein are implemented using a computer system such as that illustrated in Figure 9G. The exemplary computer system 928 is in communication with one or more computing systems 946 and / or one or more data sources 948 via one or more networks 944. While Figure 9G shows one embodiment of the computer system 928, it will be recognized that the functionality provided in the components and modules of the computer system 928 may be combined into fewer components and modules or further separated into additional components and modules.

[0222] The computer system 928 may include a plaque analysis module 940 that performs the functions, methods, acts, and / or processes described herein. The plaque analysis module 940 is executed on the computer system 928 by the central processing unit 306, which is discussed further below.

[0223] Generally, as used herein, the term "module" refers to logic embodied in hardware or firmware, or a collection of software instructions with entry and exit points. Modules are written in programming languages ​​such as JAVA, C, or C++. Software modules can be compiled or linked into executable programs, installed in dynamic link libraries, or written in interpreted languages ​​such as BASIC, PERL, LAU, PHP, or Python. Software modules can be called by other modules, by themselves, and / or in response to detected events or interrupts. Hardware-implemented modules may contain connected logic units such as gates and flip-flops and / or may include programmable units such as programmable gate arrays or processors.

[0224] Generally, modules described herein refer to logical modules that can be combined with other modules or divided into sub-modules, regardless of physical organization or storage. Modules are executed by one or more computer systems and can be stored on or in any suitable computer-readable medium, or can be implemented in whole or in part in specially designed hardware or firmware. While not all calculations, analyses, and / or optimizations require the use of a computer system, any of the methods, calculations, processes, or analyses described above can be facilitated by the use of a computer. Furthermore, in some embodiments, process blocks described herein can be modified, rearranged, combined, and / or omitted.

[0225] The computer system 928 includes one or more processing units (CPUs) 932, which may be comprised of a microprocessor. The computer system 928 further includes physical memory 936, such as random access memory (RAM) for temporarily storing information and read-only memory (ROM) for persistently storing information, and mass storage device 930, such as a backing store, hard drive, rotating magnetic disk, solid-state disk (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory, diskette, or optical media storage device. Alternatively, the mass storage device may be implemented in an array of servers. Typically, the components of the computer system 928 are connected to the computer using a standards-based bus system. The bus system may be implemented using various protocols, such as Peripheral Component Interconnect (PCI), MicroChannel, SCSI, Industrial Standard Architecture (ISA), and Extended ISA (EISA) architectures.

[0226] The computer system 928 includes one or more input / output (I / O) devices and interfaces 938, such as a keyboard, mouse, touchpad, printer, etc. The I / O devices and interfaces 938 may include one or more display devices, such as a monitor, that enable the visual presentation of data to a user. More particularly, the display devices provide, for example, the presentation of GUIs, such as application software data, and multimedia presentations. The I / O devices and interfaces 938 may also provide communication interfaces to various external devices. The computer system 928 may comprise one or more multimedia devices 934, such as, for example, speakers, a video card, a graphics accelerator, and a microphone.

[0227] [Computer System Devices / Operating Systems] Computer system 928 may run on a variety of computing devices, such as a server, a Windows server, a Structure Query Language server, a Unix server, a personal computer, a laptop computer, etc. In other embodiments, computer system 928 may run on a cluster computer system, a mainframe computer system, and / or other computing systems suitable for controlling and / or communicating with large databases, performing high-volume transaction processing, and generating reports from large databases. Computer system 928 is generally controlled and coordinated by operating system software, such as z / OS, Windows, Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, iCloud services, or other compatible operating systems, including proprietary operating systems. The operating system controls and schedules the execution of computer processes, performs memory management, provides file system, networking, I / O services, and provides a user interface, such as a graphical user interface (GUI), among other things.

[0228] [network] The computer system 928 shown in FIG. 9G is coupled to a network 944, such as a LAN, a WAN, or the Internet, via a communications link 942 (wired, wireless, or a combination thereof). The network 944 communicates with various computing devices and / or other electronic devices. The network 944 is in communication with one or more computer systems 946 and one or more data sources 948. The plaque analysis module 914 can access or be accessed by the computer systems 946 and / or data sources 948 via a web-enabled user access point. The connection can be a direct physical connection, a virtual connection, or other connection types. The web-enabled user access point can consist of a browser module that presents data textually, visually, using audio, video, and other media, and allows interaction with the data over the network 944.

[0229] The output module can be implemented as an all-points-addressable display such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, or other types and / or combinations of displays. The output module can be implemented to communicate with input devices 938, which also include software with an appropriate interface that allows a user to access data through the use of stylized screen elements such as menus, windows, dialog boxes, toolbars, and controls (e.g., radio buttons, check boxes, sliding scales, etc.). Additionally, the output module can communicate with a series of input / output devices to receive signals from a user.

[0230] [Other systems] Computer system 928 may include one or more internal and / or external data sources (e.g., data source 948). In some embodiments, one or more of the data repositories and data sources described above may be implemented using relational databases, such as DB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server, as well as other types of databases, such as flat file databases, entity-relationship databases, and object-oriented databases, and / or record-based databases.

[0231] The computer system 928 may also have access to one or more databases 948. The databases 948 may be stored in a database or data repository. The computer system 928 may access one or more databases 948 over a network 944 or may access the databases or data repositories directly via I / O devices and interfaces 938. A data repository storing one or more databases 948 may reside within the computer system 928.

[0232] [URL and Cookies] In some embodiments, including any of the embodiments disclosed herein (above or below), one or more features of the systems, methods, and devices described herein may utilize URLs and / or cookies, for example, to store and / or transmit data or user information. A uniform resource locator (URL) may include a web address and / or a reference to a web resource stored in a database and / or server. A URL may specify the location of a resource on a computer and / or computer network. A URL may include a mechanism for retrieving a network resource. A source of a network resource may receive the URL, locate the web resource, and return the web resource to the requester. A URL may be translated into an IP address, and the Domain Name System (DNS) may look up the URL and its corresponding IP address. A URL may be a reference to a web page, file transfer, email, database access, or other application. A URL may include a path, domain name, file extension, hostname, query, fragment, scheme, protocol identifier, port number, username, password, flags, object, resource name, and / or a sequence of characters that identify these. The systems disclosed herein can generate, receive, send, apply, parse, serialize, render, and / or perform actions on URLs.

[0233] Cookies, also known as HTTP cookies, web cookies, Internet cookies, or browser cookies, can contain data sent from a website and / or stored on a user's computer. This data may be stored by a user's web browser while the user browses. Cookies can contain useful information that allows a website to remember previous browsing information, such as an online store's shopping cart, button clicks, login information, and / or a record of previously visited web pages or network resources. Cookies can also contain information entered by a user, such as name, address, password, or credit card information. Cookies can also perform computer functions. For example, an authentication cookie can be used by an application (e.g., a web browser) to identify whether a user is already logged in (e.g., to a website). Cookie data can be encrypted to provide consumer security. Tracking cookies can be used to compile an individual's past browsing history. The system disclosed herein can generate and use cookies to access personal data. The system can also generate and use JSON Web Tokens to store authenticity information, HTTP authentication as an authentication protocol, IP addresses for tracking session or identity information, URLs, etc.

[0234] [Plaque Morphology / Characterization - Additional Study Details] In some embodiments, the systems, methods, and devices described herein are configured to noninvasively assess plaque morphology, e.g., using coronary computed tomography angiography (CCTA), and can be used to improve stratification of patients at risk for acute coronary syndromes (ACS). More specifically, in some embodiments, one or more low-density non-calcified plaque (LD-NCP) morphologies described herein can be identified using one or more techniques, e.g., artificial intelligence-enabled quantitative computed tomography (AI-QCT). In some embodiments, the association between one or more such low-density non-calcified plaque (LD-NCP) morphologies and ACS can be compared, confirmed, and / or strengthened with other high-risk plaque (HRP) features, e.g., napkin ring sign (NRS).

[0235] To confirm the association between LD-NCP morphology and ACS, 456 patients from the ICONIC trial were enrolled, and all patients underwent CCTA and were followed up for ACS. In this study, 226 patients developed ACS during the follow-up period. In this study, invasive coronary angiography was used to identify the culprit lesion from CCTA images at the time of ACS in 122 patients. Furthermore, this study evaluated the morphology of LD-NCP based on the shape and extent of buried LD-NCP plaque (DELP) through a retrospective analysis of CCTA images using AI-QCT.

[0236] In the example study, high-risk plaque (HRP) morphology was defined as a lesion with at least one round or bean-shaped LD-NCP deposit embedded in a 270° or greater low-density noncalcified plaque (DELP). In this example, HRP morphology was strongly associated with causative lesion precursors (aHR (adjusted hazard ratio): 8.253, [4.785, 14.234], p-value <0.001). In one example study, HRP morphology also showed a strong association with ACS (aHR: 2.484, [1.778, 3.470], p-value <0.001). In the example study, HRP morphology was detected in 40 (17.7%) patients with ACS and NRS in 12 (5.3%). In this example, the median time to ACS onset in patients without and with HRP morphology was 1845 days and 40 days, respectively (p-value <0.001).

[0237] Thus, the example study showed that HRP morphology was significantly associated with ACS and causative lesion precursors, and was a stronger predictor than other HRP features.

[0238] As noted herein, identifying patients at high risk for acute coronary syndrome (ACS) remains one of the most challenging challenges in current cardiovascular disease prevention practice. Despite a wealth of novel ASCVD risk reduction strategies, ACS incidence remains high in both primary and secondary prevention settings. Coronary CT angiography (CCTA) images can noninvasively assess coronary artery stenosis as well as atherosclerotic plaque characteristics. While the presence of obstructive coronary artery disease (CAD) can provide prognostic value, most of the causative lesion precursors of ACS may be nonobstructive. Thus, in some embodiments, a system may be configured to analyze, identify, and / or utilize the presence of one or more qualitative high-risk plaque (HRP) features, such as low-attenuation plaque, positive remodeling, patchy calcification, or napkin ring sign, as indicators of increased ACS risk. Notably, in some embodiments, the system may be configured to weight obstructive and nonobstructive HRP lesions equally or similarly with respect to ACS risk.

[0239] In some embodiments, the system can be configured to prioritize the napkin ring sign over other HRP indices because it has shown high concordance with histological findings of thin fibroatherosclerotic caps and plaque rupture. More specifically, the napkin ring sign is a morphological finding in CCTA in which low-density noncalcified plaque (LD-NCP) is completely surrounded by high-attenuation plaque. However, the clinical value of this sign may be somewhat limited due to its low prevalence and modest sensitivity.

[0240] In the example studies described herein, the association of LD-NCP shape and the degree of embedded LD-NCP with culprit lesions and ACS was evaluated and demonstrated. To this end, in the example studies, CCTA images from the multicenter ICONIC study were retrospectively analyzed using an automated, artificial intelligence (AI)-enabled, web-based software platform. In the example studies, after plaque characterization using AI, LD-NCP morphology was evaluated based on shape (e.g., round, bean-shaped, crescent-shaped, leaf-shaped, etc.) and the degree of embedded LD-NCP (DELP). Furthermore, in the example studies, the prognostic value of atherosclerotic plaque characteristics measured using AI-based plaque quantification was evaluated. Thus, by utilizing the identification and / or classification of LD-NCP morphology based on shape and DELP, some embodiments of the systems, devices, and methods described herein can be used to improve patient stratification for ACS risk.

[0241] In an example study using data from the multicenter international ICONIC (Incident Coronary Syndromes Identified by Computed Tomography) trial (NCT02959099), 234 patients who underwent coronary computed tomography angiography (CCTA) before acute coronary syndrome (ACS) during the follow-up period were matched 1:1 with a non-ACS control group who underwent CCTA. In the exemplary study, case and control patients underwent propensity score matching based on clinical risk factors for CAD, including age, male sex, history of hypertension and hyperlipidemia, family history of premature CAD, and smoking, as well as the severity of coronary artery stenosis assessed by CCTA, all of which can be used as factors and / or indicators of CAD or ACS in some embodiments of the systems, devices, and methods described herein. In the exemplary study, invasive coronary angiography (ICA) was used on CCTA images at the time of ACS to determine the underlying coronary artery lesion. In the example study, the culprit lesion could not be determined in 104 patients with ACS because ICA was not performed, ICA images were missing, or no culprit lesion was identified by ICA. In this example, all CCTA images were performed using 64 or more detector rows and in accordance with the SCCT guidelines at the time. In this example, CCTA images were subjected to artificial intelligence-assisted quantitative CT (AI-QCT) analysis, similar to some embodiments of the systems, devices, and methods described herein. The example study evaluated the association between LD-NCP morphology, culprit lesion precursors, and ACS.

[0242] In the exemplary study and in some embodiments described herein, quantitative CT analysis was performed. More specifically, in the exemplary study and in some embodiments of the systems, devices, and methods described herein, coronary arteries with a diameter greater than 1.5 mm were identified, analyzed, and segmented based on an 18-segment SCCT model. In the exemplary study and in some embodiments herein, the presence of atherosclerosis was defined as any tissue structure greater than 1 mm in the coronary artery inner wall, distinct from epicardial fat, epicardial tissue, or the coronary artery lumen. In the exemplary study and in some embodiments herein, coronary atherosclerotic plaque was characterized using an automated, artificial intelligence (AI)-enabled, web-based software platform. In the exemplary study and in some embodiments herein, Hounsfield Unit (HU) ranges were used to classify plaque volume into LD-NCP (<30 HU), non-calcified plaque (30-350 HU), and calcified plaque (>350 HU). In exemplary studies, and in some embodiments herein, percent atheroma volume was calculated as total plaque volume / coronary artery volume × 100%. In exemplary studies, and in some embodiments herein, percent LD-NCP, non-calcified plaque, and calcified plaque were calculated as specific plaque volume / total plaque volume. In exemplary studies, and in some embodiments herein, diameter stenosis was measured using AI-enabled quantitative CT (QCT).

[0243] In the exemplary study and in some embodiments herein, the morphology of LD-NCP was evaluated. Specifically, in the exemplary study and in some embodiments herein, the morphology of non-contiguous LD-NCP deposits was qualitatively evaluated by a level III qualified CCTA radiologist (JPE) and consensus agreement with an additional level III qualified radiologist (ADC) was obtained. In the exemplary study and in some embodiments herein, up to four LD-NCP deposits per coronary plaque were evaluated. In the exemplary study and in some embodiments herein, LD-NCP deposits with a maximum axial measurement of less than 0.5 mm were ignored, and plaques with LD-NCP less than 0.5 mm were not evaluated. In the exemplary study and in some embodiments herein, analysis was performed using linearized multiplanar reformat (SMPR) images with the color plaque overlay tool available in AI-QCT turned on.

[0244] In an exemplary study and in some embodiments herein, the shape of LD-NCP deposits was classified as crescent-, leaf-, bean-, and / or round based on their appearance on cross-sectional and longitudinal SMPR images. Figure 9H shows an example embodiment for assessing the morphology of low-density noncalcified plaque deposits. In an exemplary study and in some embodiments herein, the extent of embedded LD-NCP (DELP) was assessed based on the extent to which LD-NCP deposits were surrounded by noncalcified or calcified plaque. In an exemplary study and in some embodiments herein, cross-sectional images with the largest LD-NCP deposit area were used to estimate the DELP per plaque as <180°, 180-269°, 270-359°, or 360°. In the illustrated study and in some embodiments herein, high-risk plaque (HRP) morphology was defined as round or bean-shaped LD-NCP deposits with a DELP of 270° or greater.

[0245] In exemplary studies, patient demographic variables were compared between patients without and with an ACS using two-sample t-tests for continuous variables and chi-squared or Fisher's exact tests depending on sample size for categorical variables. In exemplary studies, and in some embodiments herein, LD-NCP deposit measurements were evaluated in relation to ACS.

[0246] In the example study, a robust variance estimator and marginal Cox regression analysis were used to adjust for patient clustering. In the example study, HRP morphology and atherosclerotic plaque characteristics were evaluated for association with ACS occurrence on a patient-by-patient basis using marginal Cox regression analysis. In the example study, HRP morphology and atherosclerotic plaque characteristics were evaluated for association with culprit lesion precursors on a lesion-by-lesion basis. In this example, lesion-by-lesion evaluations were performed in patients with ACS and culprit lesion determinations, comparing culprit lesions with all nonculprit lesions within patients.

[0247] In this example, event-free survival curve analysis was performed on a patient-by-patient basis, stratified not only by the maximum intralesional LD-NCP volume but also by patients with at least one HRP morphology lesion. In this example study, the Gehan-Breslow test was used to determine statistical significance. Furthermore, this example study compared HRP based on the morphology described herein, using the presence of two or more HRPs: hypoattenuated plaques, patchy calcifications, or lesions with a remodeling index of 1.1 or greater.

[0248] In the example study, hazard ratios were calculated using the time to the first ACS event or event-free follow-up days. In the example study, all hazard ratios were adjusted for interval revascularization and angina severity. In this study, a p-value of <0.05 was considered statistically significant. In the example study, continuous variables were expressed as mean ± SD, and categorical variables were expressed as numbers (percentages). In the example study, hazard ratios were expressed as HRs [95% confidence intervals]. In the example study, all statistical analyses were performed using R 4.2.0.

[0249] In this example, 456 / 468 patients (97.5%) from the ICONIC study were included, including 122 patients who underwent culprit lesion determination. In this example, 10 patients were excluded due to missing CCTA images, and 2 patients were excluded due to poor image quality. Figure 9I is a flowchart illustrating the analysis of an example study dataset validating some embodiments of the systems, devices, and methods described herein. As shown in Figure 9I, in the example study, high-risk plaque (HRP) morphology was retrospectively analyzed using data from the ICONIC study. In the example study, coronary CT angiography (CCTA) analysis was repeated using artificial intelligence-assisted quantitative CT analysis (AI-QCT) using the original images from the ICONIC study. In the example study, 456 of the 468 ICONIC patients were included in this post-hoc analysis. In the example study, the association between HRP morphology and acute coronary syndromes was evaluated on a patient-by-patient basis. In the example study, the association between HRP morphology and culprit lesion precursors was evaluated in 122 patients. In this example, all 122 patients had a culprit lesion identified at the time of ACS onset. Figure 9J shows patient demographics stratified by the occurrence of acute coronary syndrome in a study validating some embodiments of the systems, methods, and devices described herein. As shown in Figure 9J, in the exemplary study, 456 patients had a mean follow-up period of 2.71 ± 2.74 years for ACS after CCTA imaging.

[0250] Regarding lesion-specific outcomes, in an exemplary study, atherosclerotic plaque characteristics were assessed in 965 lesions from 122 patients with ACS and culprit lesion identification. Figure 9K shows atherosclerotic characteristics per lesion stratified by non-culprit lesion precursors and culprit lesion precursors in a test example validating several embodiments of the systems, methods, and devices described herein. As shown in Figure 9K, in the exemplary study, culprit lesions had plaques with significantly higher values ​​of various atherosclerotic plaque characteristics, including total plaque volume, non-calcified plaque volume, and LD-NCP volume.

[0251] Figure 9L is a forest plot of hazard ratios for the risk of culprit lesion precursors in a study validating some embodiments of the systems, methods, and devices described herein. As shown in Figure 9L, in the exemplary study, culprit lesions were identified in 122 patients at the time of an acute coronary syndrome event using invasive coronary angiography. In the exemplary study, all lesions were determined to be culprit lesion precursors on previously acquired coronary computed tomography angiography (CCTA) images. In the exemplary study and in some embodiments described herein, high-risk plaque (HRP) morphology was defined as any lesion with at least one low-density non-calcified plaque (LD-NCP) deposit that was round or bean-shaped with an embedded LD-NCP (DELP) angle of ≥ 270°. In the exemplary study and in some embodiments described herein, diameter stenosis measured using artificial intelligence quantitative computed tomography (DSAI-QCT) was evaluated for association with culprit lesion precursors. In an exemplary study, and in some embodiments described herein, lesions with a remodeling index ≥ 1.1 in which LD-NCP was present were evaluated for association with culprit lesion precursors. In the exemplary study, there were 122 culprit lesion precursors and 843 non-culprit lesion precursors. Figure 9L shows a forest plot and bar graph of the frequency of each variable as a percentage of non-culprit lesion precursors (dark) and culprit lesion precursors (light).

[0252] As shown in Figures 9K and 9L, in the study example, 19 (15.57%) of the affected lesions had HRP morphology. For example, lesions with at least one round or bean-shaped LD-NCP deposit with a DELP of 270° or more were included (adjusted hazard ratio (aHR): 8.253 [4.785, 14.234]; p-value <0.001). In the example study, the total plaque volume per lesion was 35.21 ± 87.42 mm for nonculprit lesions. 3 , 154.99±188.37mm for culprit lesions 3(aHR: 1.003 [1.003, 1.004]; p-value < 0.001). In the example study, the LD-NCP volume was 0.22 ± 2.20 mm for non-culprit lesions. 3 , 1.99±6.03mm for culprit lesions 3 In this sample, DSAI-QCT was associated with a 13.65 ± 15.32% probability of recurrence for non-malignant lesions and a 33.61 ± 22.11% probability of recurrence for malignant lesions (aHR: 1.048 [1.040, 1.055]; p<0.001).

[0253] Regarding per-patient outcomes, in the exemplary study, atherosclerotic plaque characteristics from CCTA were evaluated from 456 patients. Figure 9M shows the atherosclerotic characteristics per patient stratified by the presence or absence of acute coronary syndromes in a test example validating several embodiments of the systems, methods, and devices described herein. As shown in Figure 9M, atherosclerotic characteristics were described for 456 patients in the exemplary study: 230 patients without acute coronary syndromes (ACS) and 226 patients with ACS ((-)ACS and (+)ACS, respectively). In the example study, 40 / 47 patients (85.11%) with HRP morphology developed ACS during the follow-up period (aHR: 2.484 [1.778, 3.470]; p-value <0.001). In the example study, the total plaque burden per patient was 309.48 (333.57) mm for patients without ACS and those with ACS, respectively. 3 , 359.63(330.45)mm 3 (aHR: 1.000 [1.000, 1.001]; p-value = 0.336). In the example study, the total LD-NCP volume was 0.82 ± 2.71 mm in patients without ACS and those with ACS, respectively. 3 , 2.59±7.88mm 3In this sample, the maximum DSAI-QCT was 34.26 ± 20.93% in patients without ACS and 41.25 ± 21.97% in patients with ACS (aHR: 1.007 [1.001, 1.014]; p = 0.032).

[0254] In an example study, the adjusted hazard ratio was significantly higher for the HRP morphology definition compared with the HRP definition based on the presence of two or more HRP: hypoattenuating plaque, patchy calcification, or lesions with a remodeling index ≥ 1.1 (aHR: 1.638 [1.241, 2.486] vs. 2.484 [1.778, 3.470]; p = 0.022). Figure 9N is a forest plot of hazard ratios for association with acute coronary syndromes obtained from an example study validating several embodiments of the systems, methods, and devices described herein. As shown in Figure 9N, in the example study, hazard ratios were calculated from 226 patients who developed acute coronary syndromes (ACS) and 230 matched patients who did not develop ACS after coronary CT angiography. As shown in Figure 9N, the example forest plot illustrates the association of several variables measured from CCTA. In this study, a post-hoc analysis of the ICONIC dataset revealed high-risk plaque (HRP) morphology, diameter stenosis (DS), and AI-QCT ) ≥ 70%, DS AI-QCT ≥ 50% were assessed (red). In the exemplified studies and in some embodiments described herein, HRP was used to assess the presence of patchy calcification, a remodeling index ≥ 1.1, or low-attenuating plaque (HRP ICONIC ), presence of calcifications ≤ 3 mm or 5 cross-sectional slices (Spotty Calcifications ICONIC ), presence of a remodeling index of ≥ 1.1 (Positive Remodeling Index) ICONIC ), the presence of low-attenuation plaque (<30HU) ICONIC ), and Napkin Ring Sign ICONIC) were assessed. Additionally, as shown in Figure 9N, a bar graph from the example study shows the frequency and fraction of patients with each respective variable stratified by patients without ACS and patients with ACS (dark and light colors, respectively).

[0255] In the illustrated study and in some embodiments described herein, an acute coronary syndrome event-free survival analysis was performed. In the experimental study, HRP morphology was detected in 40 patients (17.7%) who experienced ACS and 7 patients (3.0%) who did not experience ACS; napkin ring sign (NRS) was detected in 12 patients (5.3%) who experienced ACS and 8 patients (3.5%) who did not. In this example, the median time to ACS for patients without and with HRP was 5.06 years and 0.11 years, respectively. Figure 9O shows acute coronary syndrome event-free survival rates for patients with and without high-risk plaque morphology in a study validating some embodiments of the systems, methods, and devices described herein. In Figure 9O, a patient with at least one lesion with high-risk plaque morphology (e.g., round or bean-shaped low-density noncalcified plaque (LD-NCP) deposits with an embedded LD-NCP angle of 270 degrees or greater) is shown ((+)HRP morphology). Figure 9O shows a patient without at least one HRP morphology lesion ((-) HRP morphology). In the example, the patient groups were compared using the Gehan-Breslow test. Overall, 230 patients did not develop ACS during the follow-up period, whereas 226 patients did. In this example, 47 patients had at least one HRP lesion, of which 40 developed ACS.

[0256] In exemplary studies, and in some embodiments described herein, an incremental association between low-density non-calcified plaque volume and acute coronary syndrome was observed on a patient-by-patient basis. In particular, in exemplary studies, a maximum LD-NCP lesion volume of ≥ 2 mm 3Patients with a maximum LD-NCP lesion volume of 0-1 mm were strongly associated with ACS (aHR: 1.767 [1.285, 2.430]; p-value < 0.001). 3 Patients with a low-density non-calcified plaque volume above 0.05 mm were not associated with ACS (aHR: 1.036 [0.734, 1.464]; p-value = 0.840). Figure 9P is a forest plot of the hazard ratios for the association between low-density non-calcified plaque and acute coronary syndromes from a study validating several embodiments of the systems, methods, and devices described herein. In this study, hazard ratios were calculated for 226 patients with acute coronary syndromes (ACS) and 230 matched patients without ACS after coronary CT angiography. Figure 9P shows the incremental association between maximum low-density non-calcified plaque (LD-NCP) volume per patient and lesion size. In this study, LD-NCP volumes of 0, 0-2 mm were used for the study. 3 More than 2mm 3 Patients were stratified based on the presence of at least one of the above lesions. In Figure 9P, forest plots are shown with adjusted hazard ratios and 95% confidence intervals, and bar graphs show the frequency and percentage of patients with each variable stratified by patients without ACS and patients with ACS (dark and light colors, respectively).

[0257] 9Q shows acute coronary syndrome event-free survival in patients stratified by low-density non-calcified plaque volume from a study validating some embodiments of the systems, methods, and devices described herein. In the exemplary study, and in some embodiments described herein, patients were classified into 0, >0-2, and ≥2 mm LD-NCP lesions based on the lesion with the largest volume. 3 Patients were stratified into low-density non-calcified plaque (LD-NCP) groups. In this example, the Gehan-Breslow test was used to compare patient strata, and p-values ​​were annotated. As shown in Figure 9Q, in the example study, patients with a maximum LD-NCP lesion volume of 0 mm were randomly assigned to the LD-NCP group. 3 , >0-2mm 3 , ≥ 2mm 3 The median survival times for patients with and without ESRD were 5.80 years, 3.15 years, and 0.86 years, respectively.

[0258] In the illustrated study and in some embodiments described herein, HRP defined by one or more morphology-related features and / or characteristics was used and proved to be superior to both diameter stenosis analysis and HRP definition based on two feature-positive plaques in distinguishing ACS patients from matched controls. As discussed, in the illustrated study and in some embodiments described herein, HRP morphology was strongly associated with earlier ACS occurrence, as measured by median time to ACS. Furthermore, in the illustrated study and in some embodiments described herein, on a per-lesion basis, culprit lesion precursors had greater total plaque volume, lesion length, and / or LD-NCP, noncalcified plaque, and / or calcified plaque volume. In this example and in some embodiments described herein, on a per-patient basis, ACS patients had greater LD-NCP and noncalcified plaque volume. Collectively, this example demonstrated the clinical value of HRP morphology and the effectiveness of an automated, AI-based method for measuring atherosclerotic plaque characteristics, as used in some embodiments described herein. Furthermore, in this case study and in some embodiments discussed herein, LD-NCP volumes were measured with a maximum LD-NCP lesion volume <2.0 mm 3 compared with patients with a maximum LD-NCP lesion volume of ≥ 2.0 mm 3 showed an increased association with ACS in patients with

[0259] In some embodiments, and as noted in the exemplary studies, the pathophysiology of HRP morphology is important and / or can be used as an indicator and / or characteristic of HRP. In particular, the exemplary studies and some embodiments demonstrated that crescent- and lobe-shaped LD-NCP and plaques with <270° DELP were associated with a lower risk of ACS and a lower frequency of causative lesion precursors. In the exemplary studies and some embodiments described herein, crescent- and lobe-shaped LD-NCP deposits also frequently exhibited <270° DELP. Thus, in some embodiments, plaque regions consisting of bean- or round-shaped deposits with ≥270° DELP may be identified as HRP and / or associated with ACS. In some embodiments, plaque regions consisting exclusively of bean- or round-shaped deposits may be identified as HRP and / or associated with ACS. In some embodiments, plaque regions consisting of ≥270° DELP may be identified as HRP and / or associated with ACS. In some embodiments, and in the illustrated study, LD-NCP deposits with a DELP <270° may have a greater crossing with the outer coronary artery wall. In some embodiments, plaque instability and rupture are related to the presence of thin-cap fibroatheroma (20), which is difficult to assess with CCTA due to limited spatial resolution. In some embodiments, DELP can be considered a surrogate for the presence of thin-cap fibroangiomas, and LD-NCPs with smaller DELP may be located closer to the epicardial coronary wall. Therefore, in some embodiments, a smaller DELP indicates a greater distance between the LD-NCP and the coronary artery luminal surface, indicating a lower risk of rupture. Furthermore, in some embodiments, patients with HRP morphology tend to experience ACS significantly earlier, and LD-NCP deposits with a DELP ≥270° may be more susceptible to rupture.

[0260] In some embodiments, the napkin ring sign (NRS) is considered an indicator and / or characteristic of HRP. In some embodiments, the area of ​​plaque comprising the NRS may constitute a histological counterpart of advanced plaque and thin-cap fibroatheroma. In some embodiments, the NRS is considered to be strongly associated with ACS. In some embodiments, the HRP morphological features discussed herein may share some similarities with the NRS, but the low prevalence of NRS may limit its clinical usefulness. In some embodiments, HRP morphology may provide a broader range of options for identifying significant atherosclerosis. For example, in example studies, NRS was shown to have a lower prevalence per patient compared to HRP morphology.

[0261] In some embodiments, the system is configured to identify dual-signature positive plaques as HRP. That is, in some embodiments, the system is configured to identify regions of plaque where both positive remodeling and low-attenuating plaque are present as HRP. In some embodiments, dual-signature positive plaques are believed to be associated with an increased risk of ACS.

[0262] In one study, positive plaques based on two features were directly compared with HRPs based on one or more morphological features in association with the causative lesion precursors, and in both cases, HRPs based on one or more morphological features were more strongly associated with ACS and produced far fewer false-positives.

[0263] In some embodiments, the presence of one or more HRP features, such as low-attenuation plaque (e.g., plaques less than 30 HU), positive remodeling, patchy calcification, and / or NRS, is believed to be strongly associated with adverse cardiac events. Furthermore, in some embodiments, lesions with HRP are considered to be more likely to be precursors of culprit lesions. In example studies, direct comparisons showed that patients with HRP identified based on one or more morphological features were at higher risk for ACS compared with one or more other HRP features.

[0264] In some embodiments, the system can be configured to utilize radiomics and CCTA imaging to derive and / or identify geometric features of LD-NCPs that can be used to identify high-risk coronary lesions.

[0265] As shown in the illustrated study, the median time to ACS in patients with HRP identified based on one or more morphological features was significantly reduced compared to HRP identified based on one or more other HRP features. Thus, in some embodiments, a system can be configured to utilize HRP identified based on one or more morphological features to stratify patients for high-intensity medical therapy. In some embodiments, rather than using the presence of LD-NCP alone to stratify patients for ACS risk, HRP morphological features and / or LD-NCP lesion volume can be considered to appropriately predict ACS risk.

[0266] Thus, in some embodiments, high-risk plaque morphology, e.g., based on low-density non-calcified plaque, can be used to identify patients at very high risk for acute coronary syndrome. 3Patients with lesions with a low-dense non-calcified plaque volume of 0.01 or more can be considered to be strongly associated with acute coronary syndrome. Thus, in some embodiments, the amount and morphology of low-dense non-calcified plaque can be considered when stratifying patients for risk of acute coronary syndrome.

[0267] FIG. 9R illustrates how, in some embodiments, high-risk plaque morphologies may be considered to increase the risk of acute coronary syndrome. As illustrated, in some embodiments, one or more of the high-risk plaque morphologies discussed herein may be considered to be strongly associated with acute coronary syndrome and / or causative lesion precursors. In particular, in some embodiments, crescent-shaped, low-density, non-calcified plaques may be considered not associated with the risk of acute coronary syndrome, while round or bean-shaped, low-density, non-calcified plaques may be considered to have a greater association with acute coronary syndrome. In some embodiments, quantitative coronary CT angiography based on artificial intelligence may be used to stratify patients for risk of acute coronary syndrome, for example, based on one or more morphological features described herein. Furthermore, in some embodiments, the amount of low-density, non-calcified plaque may be considered to be associated with an increased risk of acute coronary syndrome.

[0268] Specific Examples of Embodiments Relating to Plaque Morphology / Characterization The following are non-limiting examples of specific embodiments of systems and methods for determining plaque morphology and / or characteristics. Other embodiments may include one or more other or different features discussed herein.

[0269] Embodiment 1: A computer-implemented method for facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis, comprising: accessing, by a computer system, non-invasively acquired medical images of the subject; analyzing, with the computer system, medical images of the subject to identify one or more arteries; identifying, with the computer system, one or more regions of plaque in one or more coronary arteries; analyzing, with the computer system, the identified one or more regions of plaque to identify, at least in part, regions of low-density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density; analyzing one or more regions of low-density non-calcified plaque in response to identifying one or more regions of low-density non-calcified plaque, wherein analyzing one or more regions of low-density non-calcified plaque includes: determining a distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall; determining the degree of embedding of one or more low-density non-calcified plaques in one or more regions of non-calcified plaque or calcified plaque; determining the shape of one or more regions of low-density non-calcified plaque; generating, by the computer system, a display of the analysis of the one or more regions of low-density, non-calcified plaque based at least in part on the analysis of the one or more regions of low-density, non-calcified plaque, the display facilitating a determination of the subject's risk of CAD; Including, The computer system includes a computer processor and an electronic storage medium.

[0270] Embodiment 2: The computer-implemented method of embodiment 1, wherein a determination that the distance from one or more regions of low-density non-calcified plaque to the lumen wall is less than a predetermined threshold indicates an elevated risk of vulnerable plaque or CAD.

[0271] Embodiment 3: The computer-implemented method of embodiment 1, wherein the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined on a three-dimensional basis.

[0272] Embodiment 4: The computer-implemented method of embodiment 1, wherein the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined based on a two-dimensional image.

[0273] Embodiment 5: The computer-implemented method of embodiment 4, wherein the two-dimensional image is obtained by taking two-dimensional slices perpendicular to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0274] Embodiment 6: The computer-implemented method of embodiment 4, wherein the two-dimensional image is obtained by capturing a two-dimensional slice that maximizes the two-dimensional area of ​​the low-density non-calcified plaque.

[0275] Embodiment 7: The computer-implemented method of embodiment 1, wherein the distance from the one or more regions of low-density non-calcified plaque to the lumen wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the lumen wall.

[0276] Embodiment 8: The computer-implemented method of embodiment 1, wherein the distance from the one or more regions of low-density non-calcified plaque to the vessel wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the vessel wall.

[0277] Embodiment 9: The computer-implemented method of embodiment 1, wherein the one or more arteries comprise one or more coronary arteries or carotid arteries.

[0278] Embodiment 10: The computer-implemented method of embodiment 1, wherein the one or more axes of the one or more regions of low-density non-calcified plaque comprise one or more of a long axis in a longitudinal section, a short axis in a longitudinal section, a long axis in a transverse section, or a short axis in a transverse section.

[0279] Embodiment 11: The computer-implemented method of embodiment 10, wherein the one or more axes are determined on a three-dimensional basis.

[0280] Embodiment 12: The computer-implemented method of embodiment 10, wherein the one or more axes are determined based on one or more two-dimensional images.

[0281] Embodiment 13: The computer-implemented method of embodiment 12, wherein the longitudinal sections are obtained by taking two-dimensional slices parallel to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0282] Embodiment 14: The computer-implemented method of embodiment 12, wherein the longitudinal section is obtained by taking a two-dimensional slice that provides the longest longitudinal axis of the longitudinal section.

[0283] Embodiment 15: The computer-implemented method of embodiment 12, wherein the cross-sections are obtained by taking two-dimensional slices perpendicular to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0284] Embodiment 16: The computer-implemented method of embodiment 12, wherein the transverse plane is obtained by taking two-dimensional slices perpendicular to the long axis of the longitudinal plane.

[0285] Embodiment 17: The computer-implemented method of embodiment 12, wherein the cross-section is obtained by taking a two-dimensional slice that yields the largest two-dimensional area of ​​low-density non-calcified plaque.

[0286] Embodiment 18: The computer-implemented method of embodiment 1, wherein one or more of the analyses of the one or more regions of low-density non-calcified plaque are performed by a computer system.

[0287] Embodiment 19: The computer-implemented method of embodiment 1, wherein the degree of coverage of the one or more regions of low-density non-calcified plaque is determined based at least in part on visually overlaying a protractor over the one or more regions of low-density non-calcified plaque on a medical image.

[0288] Embodiment 20: The computer-implemented method of embodiment 1, wherein a high degree of embedding of one or more regions of said low-density non-calcified plaque indicates a high risk of vulnerable plaque or CAD.

[0289] Embodiment 21: The computer-implemented method of embodiment 1, wherein the shape of the one or more regions of low-density non-calcified plaque is determined as one or more of crescent, round, lobular, or bean-shaped.

[0290] Embodiment 22: The computer-implemented method of embodiment 21, wherein determining one or more regions of the low-density non-calcified plaque as round or bean-shaped indicates vulnerable plaque or a high risk of CAD.

[0291] Embodiment 23: The computer-implemented method of embodiment 1, wherein the shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on a machine learning algorithm.

[0292] Embodiment 24: The computer-implemented method of embodiment 1, wherein analyzing one or more regions of the low-density non-calcified plaque further comprises determining the length of one or more axes of the one or more regions of the low-density non-calcified plaque.

[0293] Embodiment 25: The computer-implemented method of embodiment 24, wherein the shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on one or more determined lengths of one or more axes.

[0294] Embodiment 26: The computer-implemented method of embodiment 25, wherein the shape of the one or more regions of low-density non-calcified plaque is determined at least in part based on determining a standard deviation of one or more determined lengths of one or more axes.

[0295] Embodiment 27: The computer-implemented method of embodiment 24, wherein analyzing the one or more regions of low-density non-calcified plaque further comprises determining a volume of the one or more regions of low-density non-calcified plaque, determining a volume of the one or more regions of plaque, and determining a ratio of the volume of the one or more regions of low-density non-calcified plaque to the volume of the one or more regions of plaque.

[0296] Embodiment 28: The computer-implemented method of embodiment 27, wherein the volume of the one or more regions of low-density non-calcified plaque is determined based on determining one or more lengths of one or more axes of the one or more regions of low-density non-calcified plaque.

[0297] Embodiment 29: The computer-implemented method of embodiment 27, wherein a determination that the volume of one or more regions of low-density non-calcified plaque is equal to or greater than a predetermined threshold is indicative of vulnerable plaque or a high risk of CAD.

[0298] Embodiment 30: The computer-implemented method of embodiment 27, wherein a determination that the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates unstable plaque or a high risk of CAD.

[0299] Embodiment 31: The computer-implemented method of embodiment 27, wherein a determination that the ratio of the volume of one or more regions of low-density non-calcified plaque to the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates unstable plaque or a high risk of CAD.

[0300] Embodiment 32: The computer-implemented method of embodiment 1, wherein the density comprises an absolute density.

[0301] Embodiment 33: The computer-implemented method of embodiment 1, wherein the density includes a radial density.

[0302] Embodiment 34: The computer-implemented method of embodiment 33, wherein one or more regions of plaque are identified as low-density non-calcified plaque if they have a radiodensity value between about -189 and about 30 Hounsfield units.

[0303] Embodiment 35: The computer-implemented method of embodiment 33, wherein one or more regions of plaque are identified as non-calcified plaque if the radiodensity value is between about 31 and about 350 Hounsfield units.

[0304] Embodiment 36: The computer-implemented method of embodiment 33, wherein one or more regions of plaque are identified as calcified plaque if the radiation intensity value is between approximately 351 and 2500 Hounsfield units.

[0305] Embodiment 37: The computer-implemented method of embodiment 1, wherein the medical image comprises a computed tomography (CT) image.

[0306] Embodiment 38: The computer-implemented method of embodiment 1, wherein the medical image is obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, magnetic resonance imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS).

[0307] Embodiment 39: The computer-implemented method of embodiment 1, further comprising generating, by the computer system, an assessment of the subject's risk of CAD or the risk of one or more regions of plaque based at least in part on the analysis of the one or more regions of low-density non-calcified plaque.

[0308] Embodiment 40: The computer-implemented method of embodiment 39, further comprising generating, by the computer system, a recommended treatment for the subject based at least in part on the analysis of one or more regions of low-density non-calcified plaque.

[0309] Embodiment 41: A system for facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis, the system comprising: one or more computer readable storage devices configured to store a plurality of computer executable instructions; and one or more hardware computer processors in communication with the one or more computer readable storage devices and configured to execute the plurality of computer executable instructions to cause the system to: accessing non-invasively acquired medical images of the subject; analyzing a medical image of the subject to identify one or more arteries; identifying one or more regions of plaque in one or more coronary arteries; analyzing the identified one or more regions of plaque and identifying one or more regions of low-density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density; analyzing one or more regions of low-density non-calcified plaque in response to identifying one or more regions of low-density non-calcified plaque, wherein analyzing one or more regions of low-density non-calcified plaque includes: determining a distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall; determining the degree of embedding of one or more low-density non-calcified plaques in one or more regions of non-calcified plaque or calcified plaque; determining the shape of one or more regions of low-density non-calcified plaque;

[0310] Generating a display of the analysis of the one or more regions of low-density, non-calcified plaque that facilitates determining the subject's risk of CAD based at least in part on the analysis of the one or more regions of low-density, non-calcified plaque.

[0311] Embodiment 42: The system of embodiment 41, wherein a determination that the distance from one or more regions of low-density non-calcified plaque to the lumen wall is less than a predetermined threshold indicates an elevated risk of vulnerable plaque or CAD.

[0312] Embodiment 43: The system of embodiment 41, wherein the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined on a three-dimensional basis.

[0313] Embodiment 44: The system of embodiment 41, wherein the distance from one or more regions of low-density non-calcified plaque to one or more of the lumen wall or vessel wall is determined based on a two-dimensional image.

[0314] Embodiment 45: The system described in embodiment 44, wherein the two-dimensional image is obtained by taking two-dimensional slices perpendicular to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0315] Embodiment 46: The system of embodiment 44, wherein the two-dimensional image is obtained by capturing a two-dimensional slice that maximizes the two-dimensional area of ​​the low-density non-calcified plaque.

[0316] Embodiment 47: The system of embodiment 41, wherein the distance from the one or more regions of low-density non-calcified plaque to the luminal wall is determined by determining the shortest distance between the boundary of the one or more regions of low-density non-calcified plaque and the boundary of the luminal wall.

[0317] Embodiment 48: The system of embodiment 41, wherein the distance from the one or more regions of low-density non-calcified plaque to the vessel wall is determined by determining the shortest distance between a boundary of the one or more regions of low-density non-calcified plaque and a boundary of the vessel wall.

[0318] Embodiment 49: The system of embodiment 41, wherein the one or more arteries comprise one or more coronary arteries or carotid arteries.

[0319] Embodiment 50: The system of embodiment 41, wherein the one or more axes of the one or more regions of low-density non-calcified plaque comprise one or more of a long axis in a longitudinal section, a short axis in a longitudinal section, a long axis in a transverse section, or a short axis in a transverse section.

[0320] Embodiment 51: The system described in embodiment 50, wherein the one or more axes are determined on a three-dimensional basis.

[0321] Embodiment 52: The system described in embodiment 50, wherein the one or more axes are determined based on one or more two-dimensional images.

[0322] Embodiment 53: The system described in embodiment 52, wherein the longitudinal sections are obtained by taking two-dimensional slices parallel to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0323] Embodiment 54: The system described in embodiment 52, wherein the longitudinal section is obtained by taking a two-dimensional slice that provides the longest longitudinal axis of the longitudinal section.

[0324] Embodiment 55: The system described in embodiment 52, wherein the cross-section is obtained by taking two-dimensional slices perpendicular to the longitudinal axis of a normalized multiplanar view of one or more arteries.

[0325] Embodiment 56: The system described in embodiment 52, wherein the transverse section is obtained by taking two-dimensional slices perpendicular to the long axis of the longitudinal section.

[0326] Embodiment 57: The system of embodiment 52, wherein the cross-section is obtained by taking a two-dimensional slice that provides the largest two-dimensional area of ​​low-density non-calcified plaque.

[0327] Embodiment 58: The system of embodiment 51, wherein one or more of the analyses of the one or more regions of low-density non-calcified plaque are performed by a computer system.

[0328] Embodiment 59: The system of embodiment 51, wherein the degree of coverage of the one or more regions of low-density non-calcified plaque is determined at least in part based on visually overlaying a protractor over the one or more regions of low-density non-calcified plaque on a medical image.

[0329] Embodiment 60: The system of embodiment 41, wherein a high degree of embedding of one or more regions of said low-density non-calcified plaque indicates a high risk of vulnerable plaque or CAD.

[0330] Embodiment 61: The system of embodiment 41, wherein the shape of the one or more regions of low-density non-calcified plaque is determined as one or more of crescent, round, lobular, or bean-shaped.

[0331] Embodiment 62: The system of embodiment 61, wherein determining one or more regions of the low-density non-calcified plaque as round or bean-shaped indicates vulnerable plaque or a high risk of CAD.

[0332] Embodiment 63: The system of embodiment 41, wherein the shape of the one or more regions of low-density non-calcified plaque is determined at least in part based on a machine learning algorithm.

[0333] Embodiment 64: The system of embodiment 41, wherein analyzing the one or more regions of low-density non-calcified plaque further comprises determining the length of one or more axes of the one or more regions of low-density non-calcified plaque.

[0334] Embodiment 65: The system of embodiment 64, wherein the shape of the one or more regions of low-density non-calcified plaque is determined based at least in part on one or more determined lengths of one or more axes.

[0335] Embodiment 66: The system of embodiment 65, wherein the shape of one or more regions of the low-density non-calcified plaque is determined at least in part based on determining a standard deviation of one or more determined lengths of one or more axes.

[0336] Embodiment 67: The system of embodiment 64, wherein analyzing the one or more regions of low-density non-calcified plaque further comprises determining a volume of the one or more regions of low-density non-calcified plaque, determining a volume of the one or more regions of plaque, and determining a ratio of the volume of the one or more regions of low-density non-calcified plaque to the volume of the one or more regions of plaque.

[0337] Embodiment 68: The system of embodiment 67, wherein the volume of the one or more regions of low-density non-calcified plaque is determined based on determining one or more lengths of one or more axes of the one or more regions of low-density non-calcified plaque.

[0338] Embodiment 69: The system of embodiment 67, wherein a determination that the volume of one or more regions of low-density non-calcified plaque is equal to or greater than a predetermined threshold indicates vulnerable plaque or a high risk of CAD.

[0339] Embodiment 70: The system of embodiment 67, wherein a determination that the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates unstable plaque or a high risk of CAD.

[0340] Embodiment 71: The system of embodiment 67, wherein a determination that the ratio of the volume of one or more regions of low-density non-calcified plaque to the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates unstable plaque or a high risk of CAD.

[0341] Embodiment 72: The system of embodiment 41, wherein the density comprises absolute density.

[0342] Embodiment 73: The system described in embodiment 41, wherein the density includes a radiometric density.

[0343] Embodiment 74: The system of embodiment 73, wherein one or more regions of plaque are identified as low-density non-calcified plaque if they have a radiodensity value between about -189 and about 30 Hounsfield units.

[0344] Embodiment 75: The system of embodiment 73, wherein one or more regions of plaque are identified as non-calcified plaque if they have a radiodensity value between about 31 and about 350 Hounsfield units.

[0345] Embodiment 76: The system of embodiment 73, wherein one or more regions of plaque are identified as calcified plaque if the radiation intensity value is between about 351 and 2500 Hounsfield units.

[0346] Embodiment 77: The system described in embodiment 41, wherein the medical image includes a computed tomography (CT) image.

[0347] Embodiment 78: The system described in embodiment 41, wherein the medical image is obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, magnetic resonance imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS).

[0348] Embodiment 79: The system of embodiment 41, further comprising generating, by the computer system, an assessment of the subject's risk of CAD or the risk of one or more regions of plaque based at least in part on the analysis of one or more regions of low-density non-calcified plaque.

[0349] Embodiment 80: The system of embodiment 79, further comprising generating, by the computer system, a recommended treatment for the subject based at least in part on the analysis of one or more regions of low-density non-calcified plaque.

[0350] [Container deployment] As discussed herein, disclosed herein are systems, methods, and devices for cardiovascular risk and / or disease state assessment using image-based analysis. In particular, in some embodiments, the systems, devices, and methods are the basis for cardiovascular risk and / or disease state assessment using image-based analysis of coordinates of vascular surfaces and / or associated features. In some embodiments, cardiovascular risk and / or disease state assessments generated using the systems, methods, and devices described herein can be utilized to generate patient diagnoses and / or treatment recommendations. Thus, in some embodiments, the systems, devices, and methods described herein can provide physicians and / or patients with specific quantification and / or measurement data related to a patient's plaque that does not currently exist. For example, in some embodiments, the system can provide specific numerical values ​​for stable and / or unstable plaque volume, its ratio to total vessel volume, percent stenosis, etc., using, for example, radiation intensity values ​​of pixels and / or regions within a medical image. In some embodiments, this level of detail in plaque parameters quantified from image processing and downstream analysis results can provide more accurate and useful tools for assessing patient health and / or risk in entirely new ways.

[0351] In some embodiments, the systems, devices, and methods described herein can be configured to unfold a generally cylindrical vessel, such as a coronary artery, carotid artery, and / or any other blood vessel, and flatten the vessel for further analysis. In other words, in some embodiments, a computer system is configured to computationally flatten or unfold the generally cylindrical vessel, for example, using one or more graphical or image analysis techniques. By doing so, in some embodiments, the system can more easily, more accurately, and / or more quickly assess, analyze, and / or diagnose a cardiovascular disease condition or condition, such as plaque or atherosclerosis, and / or use the same for disease tracking and / or treatment purposes. In particular, in some embodiments, by flattening and unfolding a generally cylindrical vessel, the system can be configured to expose one or more regions of plaque within the vessel. In some embodiments, the exposed portion or portions of plaque can be used to determine the risk of myocardial infarction (MI), ischemia, rapid plaque progression, plaque non-responsiveness to medication, the need for a stent, the need for a bypass graft, and / or the like.

[0352] FIG. 10A is a schematic diagram illustrating exemplary embodiment(s) of a system, method, and apparatus for cardiovascular risk and / or disease state assessment using image-based analysis of vascular surfaces and / or coordinates. As shown in the example of FIG. 10A , the system can be configured to unfold or flatten the otherwise generally cylindrical vessel of a blood vessel 1000 so that it is viewed as a rectangle 1008. More specifically, in some embodiments, the system is configured to identify a generally cylindrical vessel 1000 that may include one or more regions of plaque 1002, 1004. In some embodiments, plaque can be located within the lumen 1003 (e.g., plaque 1002). In some embodiments, plaque can be located outside the lumen 1003, either within the vessel wall 1005 or located outside the vessel 1000 adjacent the vessel outer wall 1009 (e.g., plaque 1004). When plaque is within the vessel wall, in some embodiments, the plaque causes the vessel wall to protrude into the lumen. For example, the plaque protrudes into at least a portion of the space where the lumen wall would be in the absence of the plaque, causing the vessel wall to expand into that space. In some embodiments, the system may be configured to unfold or flatten the generally cylindrical vessel of the vessel 1000 along an imaginary section line 1006 that is generally parallel to the anterior-posterior axis of the vessel 1000, e.g., an imaginary section line 1006 that lies in a plane that also includes the vessel 1000 in a longitudinal section. In some embodiments, e.g., to facilitate processing, prior to unfolding or flattening the vessel, the system is configured to generate a straightened representation of the otherwise curvilinear vessel and process the straightened representation to unfold or flatten the vessel. As an example, the width w of the representation may be the circumference of the vessel, and the length l of the representation may be the portion of the vessel being evaluated.

[0353] In some embodiments, by unfolding the vessel, the system can be configured to display and / or display the interior of the vessel as a flat or rectangular view (representation) 1008 on a user interface of a display screen, such as a graphical user display (GUI). In some embodiments, the system can be configured to display the flat or rectangular view in a three-dimensional (3D) view. In some embodiments, the system can be configured to rotate the 3D view 360 degrees to allow a medical professional to better see and evaluate the plaque. In some embodiments, doing so may expose a portion of plaque 1002 while not exposing a portion of plaque 1004. For example, in some embodiments, an area of ​​plaque facing the interior lumen of vessel 1002 may be exposed, while an area of ​​plaque 1004 facing the exterior of lumen 1003 may not be exposed. In some embodiments, by flattening or unfolding the cylindrical blood vessel 1000, the system may be configured to utilize a Cartesian or other coordinate system to characterize the surface area of ​​plaque and / or the location and / or position of different components within the area of ​​plaque, which may be used to assess a subject's cardiovascular disease risk and / or status more accurately, more quickly, and / or using less processing power. In some embodiments, the system may be configured to display the flat or rectangular representation in a three-dimensional (3D) view. In some embodiments, the system may be configured to rotate the 3D view 360° to allow a medical professional to better view and assess exposed or unexposed plaque by flattening the vessel.

[0354] In some embodiments, after unfolding or flattening the vessel, the system is configured to analyze, for example, low-density non-calcified plaque, non-calcified plaque, calcified plaque, and / or different components of a region of such plaque. In some embodiments, the location and / or position of such individual plaque components and / or the entire region of plaque itself may be analyzed by the system using one or more coordinate systems after flattening and / or unfolding the vessel.

[0355] In some embodiments, by flattening the cylindrical tube of the blood vessel or unfolding it as a rectangle, the system can be configured to visualize and / or analyze the exposed surface area in one or more ways. For example, in some embodiments, once deployed, the system may be configured to analyze, identify, color, annotate, and / or visually alter one or more of the following features: surface area of ​​plaque or exposed plaque, surface area of ​​plaque or exposed plaque compared to the total internal surface area of ​​the vessel segment, vessel and / or plaque thickness and / or its ratio, plaque composition (e.g., low-density non-calcified plaque, calcified plaque, and / or non-calcified plaque), narrowing of the vessel lumen, remodeling index, plaque heterogeneity (e.g., as a function or combination of low-density non-calcified plaque, calcified plaque, and / or non-calcified plaque), surface irregularity, surface asymmetry, surface ulceration, depth relative to one or more plaque components (e.g., low-density non-calcified plaque, calcified plaque, and / or non-calcified plaque), and / or the like.

[0356] In some embodiments, the system may be configured to apply a coordinate system to the flattened or unfolded vessel to analyze plaque and / or individual plaque components. For example, in some embodiments, the system may be configured to utilize a Cartesian coordinate system, a polar coordinate system, a cylindrical coordinate system, a spherical coordinate system, a homogeneous coordinate system, a curvilinear coordinate system, a log-polar coordinate system, a Plucker coordinate system, a generalized coordinate system, a canonical coordinate system, a varicentric coordinate system, and / or a trilinear coordinate system.

[0357] In some embodiments, the system may be configured to utilize coordinates, such as Cartesian coordinates, of plaque and / or individual plaque components to predict and / or assess cardiovascular disease risk and / or status. In some embodiments, the assessment of risk and / or disease status may depend on the location of the plaque and / or plaque components relative to one or more regions of fat, one or more other regions of plaque, the vessel lumen, the myocardium, the myocardial side of the vessel, the pericardium, the pericardial side of the vessel, epicardial fat, the epicardial fat side of the vessel, a bifurcation, a bifurcation, a trifurcation, or a distance from the ostium of the vessel. In some embodiments, the system may be configured to utilize the coordinates of the plaque and / or plaque components to determine the Euclidean distance from one or more of the foregoing.

[0358] [Plaque location relative to the myocardium v. epicardium] FIG. 10B is a schematic diagram showing an example of one or more regions of myocardium vs. epicardial fat-adjacent plaque. As illustrated in FIG. 10B , in some examples, plaque may be adjacent, proximal, adjacent, and / or proximal to myocardium vs. epicardium, which may indicate a risk of cardiovascular disease. For example, in the example shown in FIG. 10B , a first region 1024 of plaque within a blood vessel 1000 may be adjacent, proximal, nearby, and / or close to the epicardial fat side, while a second region 1022 of plaque within the same blood vessel 1000 may be adjacent, proximal, nearby, and / or close to the myocardial side. In the illustrated example, the first region 1024 of plaque closer to the epicardial side is associated with a higher risk of disease compared to the second region 1022 of plaque closer to the myocardial side. Thus, in some embodiments, the system may be configured to analyze the location of one or more regions of plaque and / or one or more components thereof, for example, by computationally unfolding the vessel and then mapping it to a coordinate system, to determine the location and / or relative location of the region of plaque or its components with respect to the epicardium and / or myocardium. In some embodiments, the analyzed location of the one or more regions of plaque and / or its one or more components may be used by the system to determine cardiovascular disease risk.

[0359] Assessment of cardiovascular risk and / or disease status by image-based analysis of vascular surface and / or feature coordinates. FIG. 10C is a flow chart illustrating an exemplary embodiment of a system, method, and apparatus for cardiovascular risk and / or disease state assessment using image-based analysis of vessel surfaces and / or coordinates.

[0360] As shown in FIG. 10C , in some embodiments, the system may be configured to access medical images at block 1032. In some embodiments, the medical images may include one or more arteries, such as the subject's coronary arteries, carotid arteries, and / or other arteries. In some embodiments, the medical images may be stored in a medical image database 1056. In some embodiments, the medical image database 1056 may be accessed locally by the system and / or may be remotely located and accessed via a network connection. The medical images may include images obtained using one or more modalities, such as CT, dual-energy computed tomography (DECT), spectral CT, photon-counting CT, X-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), magnetic resonance (MR) imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single-photon emission computed tomography (SPECT), or near-infrared spectroscopy (NIRS). In some embodiments, the medical images include one or more of contrast-enhanced CT images, non-contrast-enhanced CT images, magnetic resonance imaging, and / or images obtained using any of the aforementioned modalities.

[0361] In some embodiments, the system may be configured to automatically and / or dynamically perform one or more analyses of the medical images as discussed herein. For example, in some embodiments, at block 1036, the system may be configured to identify one or more blood vessels or arteries. The one or more arteries may include, among others, coronary arteries, carotid arteries, aorta, renal arteries, arteries of the lower limbs, arteries of the upper limbs, and / or cerebral arteries. In some embodiments, the system may be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically identify one or more arteries or coronary arteries using image processing. For example, in some embodiments, one or more AI and / or ML algorithms may be trained using a convolutional neural network (CNN) on a set of medical images in which arteries or coronary arteries have been identified, thereby enabling the AI ​​and / or ML algorithms to automatically identify the arteries or coronary arteries directly from the medical images. In some embodiments, the arteries or coronary arteries are identified by size and / or location.

[0362] In some embodiments, at block 1038, the system may be configured to identify virtual and / or computational cut lines for one or more vessels. In particular, in some embodiments, the virtual cut lines may be substantially parallel, perpendicular, and / or at another angle to the anterior-posterior axis of the vessel. In some embodiments, before identifying the virtual cut lines, the system may be configured to computationally straighten the vessel into a substantially straight cylindrical vessel shape. In some embodiments, the system may be configured to utilize one or more AI and / or ML algorithms to automatically and / or dynamically straighten the vessel and / or identify the cut lines.

[0363] In some embodiments, at block 1040, the computer system may be configured to computationally unfold the vessel, for example, along a virtual cutting line. In some embodiments, at block 1042, the system may be configured to identify one or more regions of plaque on the deployed vessel. For example, one or more regions of plaque may be exposed on the deployed vessel, which may mean that the plaque was inside the vessel before deployment. Also, in some embodiments, one or more regions of plaque may not be exposed on the deployed vessel, which may mean that the plaque was outside the vessel before deployment. In some embodiments, the system may be configured to automatically and / or dynamically identify one or more plaques on the deployed vessel using one or more AI and / or ML algorithms.

[0364] In some embodiments, at block 1044, the system may be configured to analyze one or more plaque parameters and / or one or more blood vessel or vascular parameters. In some embodiments, the one or more blood vessel and / or plaque parameters may constitute quantified parameters derived from medical images. For example, in some embodiments, the system may be configured to determine one or more blood vessel and / or plaque parameters using AI and / or ML algorithms or other algorithms. As another example, in some embodiments, the system may be configured to determine one or more blood vessel parameters, such as a classification of arterial remodeling due to plaque, which classification may further include positive arterial remodeling, negative arterial remodeling, and / or intermediate arterial remodeling. In some embodiments, the classification of arterial remodeling is determined based on the ratio of the maximum vessel diameter in the region of the plaque to a normal reference vessel diameter in the same region, which can be retrieved from a normal database. In some embodiments, the system may be configured to classify arterial remodeling as positive if the ratio of the maximum vessel diameter in the region of the plaque to the normal reference vessel diameter in the same region is 1.1 or greater. In some embodiments, the system may be configured to classify arterial remodeling as negative if the ratio of maximum vessel diameter to normal reference vessel diameter in the region of plaque is less than 0.95, hi some embodiments, the system may be configured to classify arterial remodeling as intermediate if the ratio of maximum vessel diameter to normal reference vessel diameter in the region of plaque is between 0.95 and 1.1.

[0365] Additionally, as part of block 1044, in some embodiments, the system may be configured to determine the shape and / or volume of one or more regions of plaque and / or one or more blood vessels or arteries. For example, the system may be configured to determine whether a particular region of plaque is round, oval, or some other shape. In some embodiments, the shape of a region of plaque may be a factor in assessing plaque stability. As another example, in some embodiments, the system may be configured to determine the curvature, diameter, length, volume, and / or any other parameter of a blood vessel or artery from a medical image.

[0366] In some embodiments, as part of block 1044, the system may be configured to determine the volume and / or surface area of ​​the region of plaque, and / or the ratio of the volume to the surface area of ​​the region of plaque or other function thereof, such as, for example, the diameter, radius, and / or thickness of the region of plaque. In some embodiments, a plaque with a low ratio of volume to surface area may indicate a stable plaque. Thus, in some embodiments, the system may be configured to determine that a ratio of volume to surface area of ​​the region of plaque below a predetermined threshold indicates a stable plaque.

[0367] In some embodiments, as part of block 1044, the system may be configured to determine a heterogeneity index for the region of the plaque. For example, in some embodiments, a plaque with low heterogeneity or high homogeneity may indicate a stable plaque. Thus, in some embodiments, the system may be configured to determine that heterogeneity of the region of the plaque below a predetermined threshold indicates a stable plaque. In some embodiments, the heterogeneity or homogeneity of the region of the plaque may be determined based on the heterogeneity or homogeneity of the radiodensity values ​​within the region of the plaque. Thus, in some embodiments, the system may be configured to determine the heterogeneity index of the plaque by generating a spatial mapping, such as a three-dimensional histogram of radiodensity values ​​within or across the geometry or region of the plaque. In some embodiments, if the gradient or variation of the radiodensity values ​​across the spatial mapping exceeds a certain threshold, the system may be configured to assign a high heterogeneity index. Conversely, in some embodiments, if the gradient or variation of the radiodensity values ​​across the spatial mapping is below a certain threshold, the system may be configured to assign a low heterogeneity index.

[0368] In some embodiments, as part of block 1044, the system may be configured to determine the radio density of the plaque and / or its composition. For example, a high radio density value may indicate that the plaque is highly calcified or stable, while a low radio density value may indicate that the plaque is less calcified or unstable. Thus, in some embodiments, the system may be configured to determine that a radio density of a region of the plaque that exceeds a predetermined threshold indicates stable plaque. In addition, different regions within the region of the plaque may be calcified to different levels and thereby exhibit different radio density values. Thus, in some embodiments, the system may be configured to determine the radio density value of the region of the plaque and / or the composition, proportion, or change of radio density values ​​within the region of the plaque. For example, in some embodiments, the system may be configured to determine how much or what percentage of the plaque within the region of the plaque exhibits radio density values ​​within the low range, medium range, high range, and / or any other classification.

[0369] Similarly, in some embodiments, as part of block 1044, the system may be configured to determine the ratio of the radiodensity value of the plaque to the volume of the plaque. For example, it may be important to evaluate whether large or small areas of plaque exhibit high or low radiointensity values. Thus, in some embodiments, the system may be configured to determine the percent composition of plaque consisting of different radiodensity values ​​as a function or ratio of plaque volume.

[0370] In some embodiments, as part of block 1044, the system may be configured to determine the dif...

Claims

1. 1. A computer-implemented method for facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements obtained from non-invasive medical image analysis, comprising: accessing, by a computer system, non-invasively acquired medical images of the subject; analyzing, with the computer system, medical images of the subject to identify one or more arteries; identifying, with the computer system, one or more regions of plaque in one or more coronary arteries; analyzing, by the computer system, the identified one or more regions of plaque to identify, at least in part, regions of low density non-calcified plaque, non-calcified plaque, or calcified plaque; analyzing one or more regions of low-density non-calcified plaque in response to identifying one or more regions of low-density non-calcified plaque, wherein analyzing the one or more regions of low-density non-calcified plaque includes: determining a distance from one or more regions of low density non-calcified plaque to one or more of the lumen wall or vessel wall; determining a depth of embedment of one or more low density non-calcified plaques in one or more regions of non-calcified plaque or calcified plaque; determining a shape of one or more regions of low density non-calcified plaque; generating, by the computer system, a display of the analysis of the one or more regions of low-density non-calcified plaque based at least in part on the analysis of the one or more regions of low-density non-calcified plaque that facilitates a determination of the subject's risk of CAD; Including, The method, wherein the computer system comprises a computer processor and an electronic storage medium.

2. 10. The computer-implemented method of claim 1, wherein a determination that the distance from one or more regions of low density non-calcified plaque to the lumen wall is less than a predetermined threshold indicates an increased risk of vulnerable plaque or CAD.

3. The computer-implemented method of claim 1 , wherein the distance from the one or more regions of low density non-calcified plaque to one or more of a lumen wall or a vessel wall is determined on a three-dimensional basis.

4. The computer-implemented method of claim 1 , wherein the distance from the one or more regions of low density non-calcified plaque to one or more of a lumen wall or a vessel wall is determined based on a two-dimensional image.

5. The computer-implemented method of claim 4 , wherein the two-dimensional images are obtained by taking two-dimensional slices perpendicular to a longitudinal axis of a normalized multiplanar view of one or more arteries.

6. The computer-implemented method of claim 4 , wherein the two-dimensional image is obtained by capturing a two-dimensional slice that maximizes the two-dimensional area of ​​the low-density non-calcified plaque.

7. 2. The computer-implemented method of claim 1, wherein the distance from the one or more regions of low density non-calcified plaque to the lumen wall is determined by determining the shortest distance between a boundary of the one or more regions of low density non-calcified plaque and a boundary of the lumen wall.

8. 2. The computer-implemented method of claim 1, wherein the distance from the one or more regions of low density non-calcified plaque to the vessel wall is determined by determining the shortest distance between a boundary of the one or more regions of low density non-calcified plaque and a boundary of the vessel wall.

9. The computer-implemented method of claim 1 , wherein the one or more arteries comprise one or more coronary arteries or carotid arteries.

10. 2. The computer-implemented method of claim 1, wherein the one or more axes of the one or more regions of low density non-calcified plaque comprise one or more of a long axis in a longitudinal plane, a short axis in a longitudinal plane, a long axis in a transverse plane, or a short axis in a transverse plane.

11. The computer-implemented method of claim 10 , wherein the one or more axes are determined in a three-dimensional reference.

12. The computer-implemented method of claim 10 , wherein the one or more axes are determined based on one or more two-dimensional images.

13. 13. The computer-implemented method of claim 12, wherein the longitudinal sections are obtained by taking two-dimensional slices parallel to a longitudinal axis of a normalized multiplanar view of one or more arteries.

14. The computer-implemented method of claim 12 , wherein the longitudinal section is obtained by taking a two-dimensional slice that provides the longest longitudinal axis of the longitudinal section.

15. 2. The computer-implemented method of claim 1, wherein the depth of coverage of the one or more regions of low-density non-calcified plaque is determined based at least in part on visually overlaying a protractor over the one or more regions of low-density non-calcified plaque on a medical image.

16. The computer-implemented method of claim 1 , wherein a high degree of implantation of one or more regions of low density non-calcified plaque is indicative of an increased risk of vulnerable plaque or CAD.

17. The computer-implemented method of claim 1 , wherein a shape of the one or more regions of low density non-calcified plaque is determined as one or more of crescent, round, lobe, or bean shaped.

18. 20. The computer-implemented method of claim 17, wherein determining one or more regions of low density non-calcified plaque as round or bean shaped indicates vulnerable plaque or high risk of CAD.

19. The computer-implemented method of claim 1 , wherein the shape of the one or more regions of low density non-calcified plaque is determined based at least in part on a machine learning algorithm.

20. 2. The computer-implemented method of claim 1, wherein analyzing the one or more regions of low density non-calcified plaque further comprises determining a length of one or more axes of the one or more regions of low density non-calcified plaque.

21. 21. The computer-implemented method of claim 20, wherein a shape of the one or more regions of low density non-calcified plaque is determined based at least in part on the one or more determined lengths of one or more axes.

22. 22. The computer-implemented method of claim 21, wherein the shape of the one or more regions of low density non-calcified plaque is determined based at least in part on determining a standard deviation of the determined one or more lengths of the one or more axes.

23. Analyzing one or more regions of low density non-calcified plaque includes: determining a volume of one or more regions of said low density non-calcified plaque; determining a volume of one or more regions of plaque; 21. The computer-implemented method of claim 20, further comprising determining a ratio of a volume of the one or more regions of low density non-calcified plaque to a volume of the one or more regions of plaque.

24. 24. The computer-implemented method of claim 23, wherein a volume of the one or more regions of low density non-calcified plaque is determined based on determining one or more lengths of one or more axes of the one or more regions of low density non-calcified plaque.

25. 24. The computer-implemented method of claim 23, wherein a determination that the volume of one or more regions of low density non-calcified plaque is equal to or greater than a predetermined threshold indicates an elevated risk of vulnerable plaque or CAD.

26. 24. The computer-implemented method of claim 23, wherein a determination that the volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates an elevated risk of unstable plaque or CAD.

27. 24. The computer-implemented method of claim 23, wherein a determination that a ratio of a volume of one or more regions of low density non-calcified plaque to a volume of one or more regions of plaque is equal to or greater than a predetermined threshold indicates an elevated risk of unstable plaque or CAD.

28. The computer-implemented method of claim 1 , wherein the density comprises an absolute density.

29. The computer-implemented method of claim 1 , wherein the density comprises a radial density.

30. 2. The computer-implemented method of claim 1, wherein the medical images are obtained using imaging techniques including one or more of CT, X-ray, ultrasound, echocardiography, magnetic resonance imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), or near infrared spectroscopy (NIRS).

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