Systems and methods for characterizing high-risk plaques

Abstract

A method of characterizing coronary plaque tissue data and perivascular tissue data using image data collected from computed tomography (CT) scans along a blood vessel, the image information including radiodensity values of a coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method including quantifying radiodensities in a region of the coronary plaque, quantifying radiodensities in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining a gradient of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, and determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing the gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular and / or the ratio of the coronary plaque radiodensity values and the corresponding perivascular tissue radiodensity values.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 979,024, filed January 25, 2019, entitled “SYSTEMS AND METHOD OF CHARACTERIZING HIGH RISK PLAQUES,” and U.S. Non-Provisional Application No. 16 / 750,278, filed January 23, 2020, entitled “SYSTEMS AND METHODS FOR CHARACTERIZING HIGH RISK PLAQUES.” Each of the foregoing applications is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] The systems and methods disclosed herein relate to identifying high-risk plaques in coronary arteries, and more specifically to characterizing coronary plaques by using a three-dimensional (3D) model of a coronary medical image to calculate a density ratio of a coronary plaque and surrounding tissue. BACKGROUND

[0004] The heart is a muscle that receives blood from several arteries, including the left circumflex artery (LCX) and the left anterior descending artery (LAD), both of which branch off from the left main artery and the right coronary artery. Coronary artery disease is often associated with a constriction or blockage of one of these arteries, and can create coronary lesions in the blood vessels that supply blood to the heart, such as stenosis (abnormal narrowing of a blood vessel) or ischemia (insufficient blood supply to a part of the body due to obstruction of inflow of arterial blood). As a result, blood flow to the heart can be limited. Patients with coronary artery disease can experience chest pain. More severe manifestations of coronary artery disease can lead to myocardial infarction or heart attack.

[0005] Patients with chest pain and / or exhibiting symptoms of coronary artery disease can undergo one or more tests that can provide some indirect evidence related to coronary lesions. For example, non-invasive tests can include electrocardiogram, blood tests, treadmill exercise test, echocardiogram, single positron emission computed tomography (SPECT), and positron emission tomography (PET). Anatomical data can be non-invasively acquired using coronary computed tomography angiography (CCTA), which uses computed tomography (CT) scans to examine the arteries that supply blood to the heart after intravenous infusion of an iodine-containing contrast agent and determine whether they have been narrowed or blocked by plaque buildup. CCTA and CT scans, which are sometimes used herein, can be referred to as CT scans hereinafter for brevity. Images generated from CT scans can be reconfigured to create three-dimensional (3D) images that can be viewed on a monitor, printed on film, or transferred to electronic media. Invasive tests can include measuring fractional flow reserve (FFR) of any given lesion to assess its functional significance. FFR is defined as the ratio of blood pressure downstream of a lesion when hyperemic to blood pressure upstream of the lesion, and measurement requires a cardiac catheter. Another common invasive test is invasive coronary angiography, which scores heart disease severity through the SYNTAX scoring system, which involves rating coronary artery anatomy using angiography and answering a series of questions. A score is generated based on lesion characteristics and responses to questions to determine the most appropriate course of treatment. This process is very time consuming, and depends on the subjective characterization of coronary angiography by individual cardiologists. Because of this limitation, the SYNAX score is sometimes performed by multiple cardiologists to obtain an average score, which increases the time and resources required to perform the assessment. Furthermore, like any invasive medical procedure, FFR and SYNTAX scoring carry the risk of having adverse effects and unnecessary medical expenses.

[0006] Although plaque characteristics such as adverse plaque characteristics (APC) have been studied for prognostic value of major adverse cardiac events using both invasive and non-invasive techniques such as intravascular ultrasound, optical coherence tomography, and coronary computed tomography data, there is a need for methods and systems for predicting adverse cardiac events by characterizing individual coronary plaque using non-invasive imaging techniques (patient-specific atomic image data). SUMMARY

[0007] Coronary artery disease (CAD) is a major cause of morbidity and mortality. Coronary computed tomography angiography (CCTA, sometimes simply CT) has become a non-invasive method for assessing CAD. Coronary atherosclerosis is the primary disease entity of CAD, and coronary stenosis and ischemia are secondary and tertiary consequences of the atherosclerotic process. The primary mechanism by which coronary atherosclerosis leads to heart attack is plaque rupture, plaque erosion, or protrusion of a calcified nodule through the plaque. Coronary atherosclerosis can occur in many different forms: focal or diffuse, at the bifurcation or trifurcation of the artery / along the straight segment of the artery; with different composition, e.g., plaque can be classified into necrotic core, fibrofatty, fibrous, calcified, and dense calcified; and with different 3D shapes. Not all coronary plaques will be involved in adverse cardiac events, and over-treatment of non-risk plaques can put patients at unnecessary health risk and can lead to unnecessary increase in medical care costs. Described herein are methods and systems for using images generated by scanning a patient's arteries (e.g., CCTA) to identify coronary plaques that have a higher risk of causing future heart attack or acute coronary syndrome. While it is not necessarily possible to determine whether these plaques will rupture, erode, or protrude, it can be noted that these plaques are likely to be the culprit of future heart attack or acute coronary syndrome (ACS).

[0008] Scientific evidence reported to date suggests that examining "high-risk plaque" features focuses only on the plaque, and certain atherosclerotic plaque features have been identified to be associated with risk of future heart attack or ACS, such as aggregate plaque volume (APV), low-attenuation plaque (LAP), positive remodeling (PR), and napkin ring sign (NRS). However, these studies fail to consider the relationship between the plaque and the adjacent vessel structure, most notably the coronary lumen and the perivascular coronary fat / tissue.

[0009] As used herein, "radiodensity" is a broad term that refers to the relative inability of an electromagnetic relationship (e.g., X-rays) to pass through a material. In relation to an image, a radiodensity value refers to a value indicative of a density in image data (e.g., film, print, or image data in electronic format), where the radiodensity value in the image corresponds to the density of the material depicted in the image. As used herein, "attenuation" is a broad term that refers to the gradual loss of intensity (or flux) through a medium. "Low attenuation" is a term that can be used to indicate that portions of the material in the image having low density appear darker in the image. "Hyperattenuation" is a term that can be used to indicate that portions of the material in the image having high density appear brighter in the image. Antoniades reports a fat attenuation index, FAI, which can identify high-risk plaques by characterizing the radiodensity gradient of fat tissue (e.g., perivascular fat tissue) proximate or surrounding such plaques. (See Antonopoulos et al., "Detecting human coronary inflammation by imaging perivascular fat," Sci. Transl. Med., Vol. 9, Issue 398, July 12, 2017) The attenuation density of fat cells near water density (e.g., Hounsfield Unit (HU) density = 0) contrasts with fat cells of lower HU density (near -100), the former being associated with more inflamed fat cells and achieving higher attenuation due to cholesterol efflux from these cells. This is believed to be to identify sites of more inflamed coronary atherosclerotic lesions.

[0010] However, no studies to date have considered the relationship between the density of the coronary lumen, the plaque itself, and the perivascular coronary fat. As outlined below and described herein, methods and systems for identifying coronary plaques that are implicated in increased susceptibility to future ACS, heart attack, or death are described. In some embodiments, a ratio method is described, in which the plaque acts as a central fulcrum between the lumen or the perivascular coronary fat.

[0011] One innovation includes a method for characterizing (e.g., volumetrically characterizing) a coronary plaque using data from images of the coronary plaque and perivascular tissue adjacent to the coronary plaque collected from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of the coronary plaque and the perivascular tissue adjacent to the coronary plaque. In some embodiments, the perivascular tissue can include a blood vessel lumen and / or perivascular fat. In some embodiments, the method can include creating a three-dimensional (3D) model from the CT images prior to determining the radiodensity values. In some embodiments, the method can include quantifying radiodensity in a region of the coronary plaque in the image data, quantifying radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data, determining a gradient of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue, determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue, and characterizing the coronary plaque based at least on the radiodensity values and / or the ratio. In some examples, the plaque can be characterized by analyzing one or more of a minimum radiodensity value of the plaque and / or the perivascular tissue and a maximum radiodensity value of the plaque and / or the perivascular tissue. In some embodiments, the perivascular tissue can include at least one of a coronary vessel lumen, fat, or the coronary plaque. Such a method is performed by one or more computer hardware processors configured to execute computer executable instructions on a non-transitory computer storage medium. Such a method can include one or more other aspects, or in different embodiments, aspects of the method can be characterized in a number of ways, some of which are described below.

[0012] In some embodiments, the method further includes receiving the image data at the data storage component via a network. In some embodiments, the network is one of the Internet or a wide area network (WAN). In some embodiments, the image data from the CT scan includes at least ten images, or at least 30 images, or more. In some embodiments, the method further includes generating a patient report based on the characterization of the coronary plaque, the patient report including at least one of a diagnosis, a prognosis, or a recommended treatment for the patient.

[0013] Adipose tissue (or simply "fat") is a connective tissue that plays an important role in body function by storing energy in the form of lipids and buffering and insulating the body. It is a loose connective tissue composed primarily of adipocytes, but can also contain stromal vascular fraction (SVF) of cells, including preadipocytes (precursor cells to adipocytes), fibroblasts, and vascular endothelial cells, as well as various immune cells. Quantifying radiodensity in at least one region of perivascular tissue can include quantifying radiodensity of coronary artery plaque and adipose tissue in one or more regions or layers of the coronary artery plaque and / or perivascular tissue. In some embodiments, the radiodensity of the scan information is quantified against water in each of one or more of the regions of the coronary artery plaque and perivascular tissue (e.g., as a control or reference point). In some embodiments, the radiodensity of the scan information is quantified against necrotic core plaque in each of one or more regions or layers of the coronary artery plaque. In some embodiments, the coronary artery plaque radiodensity value and the perivascular tissue radiodensity value are mean radiodensities. In some embodiments, the coronary artery plaque radiodensity value and the perivascular tissue radiodensity value are maximum radiodensities. In some embodiments, the coronary artery plaque radiodensity value and the perivascular tissue radiodensity value are minimum radiodensities. The quantified radiodensities can be characterized as numerical values. In some embodiments, the quantified radiodensities take into account CT scan and patient-specific parameters, including but not limited to one or more of the following: iodine-containing contrast agent, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal-to-noise ratio, contrast-to-noise ratio, tube voltage, milliamp, cardiac gating method, CT scanner type, heart rate, heart rhythm, or blood pressure.

[0014] Such a method can also include reporting the quantified radiodensities of the coronary artery plaque and the perivascular tissue as a gradient of such radiodensities. In some embodiments, the quantified radiodensities of the coronary artery plaque and the perivascular tissue are determined and reported as a ratio of the slope of the radiodensity gradient of the coronary artery plaque and the perivascular tissue adjacent to the coronary artery plaque. In some embodiments, the quantified (maximum or minimum) radiodensities of the coronary artery plaque and the perivascular tissue are determined and each is reported as a difference in radiodensity values of the coronary artery plaque and the perivascular tissue. In some embodiments, the image data is collected from a CT scan along a length of at least one of the right coronary artery, the left anterior descending artery, the left circumflex artery, the aorta, the carotid artery, or the femoral artery, or a branch thereof. In some embodiments, the data is collected from a CT scan along a length of a non-coronary reference vessel (e.g., which can be the aorta). The radiodensities of the image data can be expressed using a variety of measurement units. In one example, the radiodensities are quantified in Hounsfield units. In another example, the radiodensities are quantified in absolute material density, for example, when performing multi-energy CT, which uses spectral data that allows for differentiation and classification of tissues to obtain material-specific images.

[0015] In some embodiments of the method for characterizing (e.g., volumetrically characterizing) coronary plaque, one or more regions (or layers) of perivascular tissue extend to an end distance from the outer wall of the blood vessel. In some embodiments of the method, one or more regions (or layers) of coronary plaque tissue extend to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity of adipose tissue (i) reaches a maximum value within the plaque, or (ii) increases by a relative percentage (e.g., >10%); or (iii) changes by a relative percentage from the lowest radiodensity value in the plaque. In some embodiments, the end distance can be defined as a fixed distance at which the radiodensity of adipose tissue (i) reaches a minimum value within the scanned anatomical site in a healthy blood vessel, or (ii) decreases by a relative percentage (e.g., >10%); or (iii) decreases by a relative percentage from the baseline radiodensity value in the same type of blood vessel without disease. In some embodiments, the baseline radiodensity value is the radiodensity quantified in a layer of adipose tissue located within a fixed layer or region surrounding the outer blood vessel wall, measured by thickness, area, or volume. In some embodiments, the baseline perivascular tissue radiodensity is the radiodensity quantified for a layer of adipose tissue located proximal to the outer blood vessel wall. In some embodiments, the baseline adipose tissue radiodensity is the radiodensity quantified for water in a layer of adipose tissue located proximal to the outer blood vessel wall (as a reference or control point). The baseline radiodensity can be generated in various ways. In some embodiments, the baseline radiodensity is an average radiodensity. In some embodiments, the baseline radiodensity is a maximum radiodensity. In some embodiments, the baseline radiodensity is a minimum radiodensity. In some embodiments, the baseline coronary plaque radiodensity value is an average radiodensity quantified in a layer of coronary plaque tissue within a fixed layer or region within the plaque, and measured by thickness, area, or volume. In some embodiments, the baseline coronary plaque radiodensity is the radiodensity quantified for all coronary plaque in the measured blood vessel.

[0016] In some embodiments, the method can further include determining a plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, from the most distal to the endmost distance, relative to the baseline radiodensity in each of the one or more concentric layers of the perivascular tissue; determining an area of a region bounded by the plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, from the most distal to the endmost distance, and the plot of the baseline radiodensity as a function of distance from the outer wall of the vessel, from the most distal to the endmost distance; and dividing the area by the quantified radiodensity measured at a distance from the outer wall of the vessel, wherein the distance is less than the radius of the vessel, or is a distance from the outer surface of the vessel above which the quantified radiodensity of the adipose tissue decreases more than 5% compared to the baseline radiodensity of the adipose tissue in a non-diseased vessel of the same type. Some embodiments of the method can further include determining a plot of the quantified change in radiodensity as a function of distance from the outer wall of the vessel, from the most distal to the inner surface of the plaque, relative to the baseline radiodensity in each of the one or more concentric layers of the coronary plaque tissue; determining an area of a region bounded by the plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, from the most distal to the inner surface of the plaque, and the plot of the baseline radiodensity as a function of distance from the outer wall of the vessel, from the most distal to the inner surface of the plaque; and dividing the area by the quantified radiodensity measured at a distance from the outer wall of the vessel, wherein the distance is less than the radius of the vessel, or is a distance from the outer surface of the vessel above which the quantified radiodensity of the adipose tissue decreases more than 5% compared to the baseline radiodensity of the adipose tissue in a non-diseased vessel of the same type. In some embodiments, the quantified radiodensity is a quantified radiodensity of adipose tissue in each of the one or more regions or layers of the perivascular tissue or coronary plaque. In some embodiments, the quantified radiodensity is a quantified radiodensity of water in each of the one or more regions or layers of the perivascular tissue. In some embodiments, the quantified radiodensity is an average radiodensity. In some embodiments, the quantified radiodensity is a maximum radiodensity. In some embodiments, the quantified radiodensity is a minimum radiodensity.

[0017] In some embodiments of the methods described herein, the method can further include normalizing the quantified radiodensity of the coronary artery plaque and perivascular tissue for CT scan parameters (patient and CT specific parameters), including but not limited to one or more of: iodine containing contrast, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal to noise ratio, contrast to noise ratio, tube voltage, milliamp, cardiac gating method, CT scanner type, heart rate, heart rhythm, or blood pressure. In some embodiments, the method can further include normalizing the quantified radiodensity of the perivascular fat associated with the coronary artery plaque for remote perivascular fat, and normalizing the quantified radiodensity of the coronary artery plaque for remote coronary plagues.

[0018] Embodiments of the method can further include quantifying other high-risk plaque features, such as remodeling, volumn, and punctate calcification, and further characterizing the high-risk plaque based on one or more of the high-risk plaque features. In some embodiments, the characterization of the coronary artery plaque includes analyzing plaque heterogeneity, in particular the presence of a mixture of calcified and non-calcified plaque. In some embodiments, characterizing the coronary artery plaque includes identifying the coronary artery plaque as a high-risk plaque if the coronary artery plaque is susceptible to involvement as a culprit lesion for a future acute coronary event based on a comparison to previously classified patient image data, which can include scan image data taken for the same patient and / or scan image data taken for other patients. In some embodiments, characterizing the coronary artery plaque includes identifying the coronary artery plaque as a high-risk plaque if the coronary artery plaque is likely to cause ischemia (e.g., a restriction of blood supply to tissue) based on a comparison to previously classified patient image data. In some embodiments, characterizing the coronary artery plaque includes identifying the coronary artery plaque as a high-risk plaque if the coronary artery plaque is likely to show distortion of shear stress (e.g., low shear stress) based on a comparison to previously classified patient image data.

[0019] Vasospasm is a narrowing of an artery caused by sustained constriction of the blood vessel, which is referred to as vasoconstriction. This narrowing can reduce blood flow. Vasospasm can affect any part of the body, including the brain (cerebral vasospasm) and the coronary arteries (coronary vasospasm). In some embodiments of the method, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary plaque is likely to cause vasospasm. In some embodiments, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary plaque is likely to progress rapidly. In some cases, a coronary plaque can calcify, hardening by depositing or transforming into calcium carbonate or other insoluble calcium compounds. In some embodiments, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary plaque is likely to not calcify. In some embodiments, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary plaque is likely to not respond to medical therapy, regress, or stabilize. In some embodiments, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque if the coronary plaque progresses rapidly in size in volume. In some embodiments, characterizing the coronary plaque includes identifying the coronary plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary plaque is associated with complications at the time of revascularization, such as by inducing the no-reflow phenomenon.

[0020] Another innovation includes a system for characterizing coronary plaque tissue (e.g., volumetric characterization) using image data collected from one or more computed tomography (CT) scans along a blood vessel, the image information including radiodensity values of the coronary plaque and of surrounding tissue adjacent to the coronary plaque. The system can include a first non-transitory computer storage medium configured to store at least the image data, a second non-transitory computer storage medium configured to store at least computer-executable instructions, and one or more computer hardware processors in communication with the second non-transitory computer storage medium. The one or more computer hardware processors are configured to execute the computer-executable instructions to at least: quantify radiodensity in a region of the coronary plaque in the image data; and quantify radiodensity in at least one region of corresponding surrounding tissue adjacent to the coronary plaque in the image data; determine a gradient of the quantified radiodensity values within the coronary plaque and within the corresponding surrounding tissue; determine a ratio of the quantified radiodensity values within the coronary plaque and the corresponding surrounding tissue; and characterize the coronary plaque by analyzing one or more of: the gradient of the quantified radiodensity values in the coronary plaque and the corresponding surrounding tissue, or the ratio of the coronary plaque radiodensity values and the corresponding surrounding tissue radiodensity values.

[0021] Another innovation includes a non-transitory computer-readable medium including instructions that, when executed, cause one or more hardware computer processors of an apparatus to perform a method including quantifying radiodensity in a region of a coronary plaque in image data. The method can also include quantifying radiodensity in at least one region of corresponding surrounding tissue adjacent to the coronary plaque in the image data; determining a gradient of the quantified radiodensity values within the coronary plaque and within the corresponding surrounding tissue. The method can also include determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding surrounding tissue. The method can also include characterizing the coronary plaque by analyzing one or more of: the gradient of the quantified radiodensity values in the coronary plaque and the corresponding surrounding tissue, or the ratio of the coronary plaque radiodensity values and the corresponding surrounding tissue radiodensity values. BRIEF DESCRIPTION OF DRAWINGS

[0022] The disclosed aspects will now be described by way of example, with reference to the accompanying drawings, in which:

[0023] Figure 1A diagram depicting an example of an embodiment of a system 100 including a processing system 120 configured to characterize coronary artery plaque.

[0024] Figure 2 is a diagram illustrating an example of heart muscle and its coronary arteries.

[0025] Figure 3 illustrates an example of a set of images generated from a scan along a coronary artery, including a selected image of a portion of the coronary artery, and how image data can correspond to values on a Hounsfield scale.

[0026] Figure 4A is a block diagram illustrating a computer system 400 upon which various embodiments can be implemented.

[0027] Figure 4B is a block diagram illustrating computer modules in a computer system 400 that can implement various embodiments.

[0028] Figure 5A illustrates an example of a flowchart of a process for analyzing coronary artery plaque.

[0029] Figure 5B illustrates an example of a flowchart of a portion of the flowchart in Figure 5A for determining characteristics of coronary artery plaque.

[0030] Figure 6 illustrates a representation of image data depicting an example of a portion of a coronary artery 665 (sometimes referred to herein as a "vessel" for ease of reference).

[0031] Figure 7 illustrates the same vessel 665 as shown in Figure 6 and plaque and fat features, and further shows additional examples of portions of the artery and plaque and / or perivascular fat proximate to the artery that can be analyzed to determine characteristics of the patient's artery.

[0032] Figure 8 illustrates an example of a region that can be evaluated to characterize plaque, including a portion of a coronary artery and perivascular tissue adjacent to the coronary artery.

[0033] Figure 9 illustrates an example of an overview of a representation of image data of a coronary artery (vessel) 905.

[0034] Figure 10 illustrates an example of a representation of image data of a coronary artery (vessel) 905. Figure 9Another view of the representation of the image data of the coronary artery (vessel) 905 shown, showing examples of certain features of the plaque, the perivascular tissue (e.g., fat), and the lumen that can be evaluated to characterize the coronary artery plaque that determines the health characteristics of the patient's artery.

[0035] Figure 11 Another example of determining radiodensity values of perivascular fat and plaque regions to determine a metric is illustrated, as described herein.

[0036] Figure 12 A representation of image data showing the coronary artery 905, plaque 915, and perivascular fat 920 adjacent to the plaque (as Figure 11 shown similarly in FIG. 9B) is illustrated.

[0037] Figure 13 is a table showing examples of a set of patient information.

[0038] Figure 14 is a table 1400 showing examples of a set of scan information.

[0039] Figure 15 is a table 1500 showing examples of a set of heart information.

[0040] Figure 16 is an example of a cross-section of a coronary artery 1600. The coronary artery includes the internal lumen of the artery and the outer vessel wall, exhibiting a gradient radiodensity in the lumen, within the plaque, and in the perivascular tissue outside the vessel.

[0041] Figure 17 is an image showing an example of a longitudinal straightened rendering of a coronary artery 1708, showing plaque buildup between the interior and exterior of the coronary artery 1708. The figure demonstrates different compartments of the lumen, plaque, and perivascular tissue.

[0042] Figure 18 is a chart illustrating a plot of compartment areas of cross-sections of plaque 1801, lumen 1802, and fat 1803 along the length of a coronary artery. Different ratios of these compartments can be calculated in area or total volume.

[0043] Figure 19 is a chart illustrating another plot of compartment areas of cross-sections of plaque 1901, lumen 1902, and fat 1903 along the length of a coronary artery. DETAILED DESCRIPTION

[0044] Introduction

[0045] Methods for identifying high-risk plaques using volumetric characterization of coronary plaque and perivascular adipose tissue data from computed tomography (CT) scans are disclosed. Volumetric characterization of coronary plaque and perivascular adipose tissue allows determination of the inflammatory state of the plaque from CT scans. This can be used for diagnosis, prognosis, and treatment of coronary artery disease. While certain example embodiments are shown in the drawings and will be described in detail herein, these embodiments are capable of various modifications and alternative forms. It should be understood that the example embodiments are not intended to be limited to the particular forms disclosed, but rather, the example embodiments will cover all modifications, equivalents, and alternatives falling within the scope of the example embodiments.

[0046] It will be understood that, although the terms first, second, etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used 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 example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0047] It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements can be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.).

[0048] Spatially relative terms, such as "below," "beneath," "lower," "above," "upper" and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientations depicted in the figures. The devices can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein are to be interpreted in accordance with the change of

[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the terms “comprises,” “comprising,” “includes,” and / or “including” as used herein specify the presence of the stated feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. In this specification, the term “and / or” selects each individual item and all combinations thereof.

[0050] The exemplary embodiments are described herein with reference to cross-sectional illustrations as schematic representations of idealized embodiments (and intermediate structures). Therefore, variations in the illustrated shapes can be expected due to, for example, manufacturing techniques and / or tolerances. Thus, the embodiments should not be construed as limited to the specific shapes of the regions shown herein, but rather include, for example, shape deviations due to manufacturing processes. For example, an implantation region illustrated as rectangular will typically have circular or curved features and / or an implantation concentration gradient at its edges, rather than a binary variation from an implanted region to a non-implanted region. Similarly, a buried region formed by implantation may result in some implantation in the region between the buried region and the surface through which implantation takes place. Therefore, the regions shown in the figures are schematic in nature, and their shapes are not intended to illustrate the actual shapes of the regions of the device and are not intended to limit the scope of the exemplary embodiments.

[0051] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the exemplary embodiments pertain. It should be further understood that terms such as those defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the relevant field, and not as having an idealized or overly formal meaning, unless expressly defined herein.

[0052] It should also be noted that in some alternative implementations, the indicated functions / actions may not occur in the order shown in the diagram. For example, depending on the functions / actions involved, two diagrams shown successively may actually be executed substantially simultaneously or sometimes in reverse order.

[0053] When it is determined that a detailed description related to a related known function or configuration can unnecessarily obscure the purpose of the example embodiments, a detailed description thereof can be omitted. Also, the terms used herein are defined to appropriately describe the example embodiments and thus can be changed according to the user, operator's intention or custom. Therefore, the terms must be defined based on the following general description in the specification.

[0054] In the drawings, the size of layers and regions is exaggerated for clarity. It will also be understood that when a layer (or layers) is referred to as being "on" another layer or substrate, it can be directly on the other layer or substrate, or intervening layers can also be present. In addition, it will also be understood that when a layer is referred to as being "beneath" another layer, it can be directly beneath, and that there can also be intervening layers present. In addition, it will also be understood that when a layer is referred to as being "between" two layers, it can be the only layer between the two layers, or one or more intervening layers can also be present. Like reference numerals refer to like elements throughout.

[0055] Overview of an example processing system for characterizing coronary artery plaque

[0056] The present disclosure includes methods and systems that use data generated from images collected by scanning a patient's arteries to identify coronary artery plaques that have a higher risk of causing future heart attacks or acute coronary syndromes. In particular, properties of perivascular coronary fat, coronary artery plaque, and / or coronary artery lumen, and relationships between perivascular coronary fat, coronary artery plaque, and / or coronary artery lumen properties are discussed to determine ways to identify coronary artery plaques that are more likely to be involved in future ACS, heart attacks, and death. The images used to generate the image data can be CT images, CCTA images, or images generated using any suitable technique that is capable of depicting the relative densities of coronary artery plaque, perivascular fat, and coronary artery lumen. For example, CCTA images can be used to generate two-dimensional (2D) or volumetric (three-dimensional (3-D)) image data, and this image data can be analyzed to determine certain properties associated with the radiodensities of coronary artery plaque, perivascular fat, and / or coronary artery lumen. In some implementations, Hounsfield scale is used to provide a measure of the radiodensities of these features. As is well known, Hounsfield units represent an arbitrary unit of X-ray attenuation used for 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 then these values characterizing these features can be analyzed.

[0057] In various embodiments, the processing of image information can include: (1) determining scan parameters (e.g., mA (milliamp), kvP (peak kilovoltage)); (2) determining scan image quality (e.g., noise, signal-to-noise ratio, contrast-to-noise ratio); (3) measuring scan-specific coronary artery lumen density (e.g., from a point distal to the coronary artery wall to a point proximal to the coronary artery wall to the distal coronary artery, and from a central location to an outer location of the coronary artery (e.g., outer relative to radial distance from the coronary artery)); (4) measuring scan-specific plaque density as a function of its 3D shape (e.g., from center to outer, sudden changes from high to low or low to high within the plaque); and (5) measuring scan-specific perivascular coronary artery fat density (from close to the artery to far from the artery) as a function of its 3D shape.

[0058] From these measurements, independent of any known characteristics of atherosclerosis causing ischemia, we can determine a number of characteristics, including but not limited to:

[0059] 1. The ratio of lumen attenuation to plaque attenuation, where the volume model of scan-specific attenuation density gradient within the lumen is adjusted for reduced lumen density throughout the plaque lesion that is functionally more significant in terms of risk value.

[0060] 2. The ratio of plaque attenuation to fat attenuation, where plaque with high radiodensity is considered to be of lower risk, even in the subset of plaques considered to be “calcified,” where

[0061] There can be a density gradient (e.g., 130 to 4000 HU) and risk is considered to decrease with increasing density.

[0062] 3. The ratio of lumen attenuation / plaque attenuation / fat attenuation

[0063] 4. The ratio of #1-3 as a function of the 3D shape of atherosclerosis, where 3D texture analysis of the plaque can be included

[0064] 5. The 3D volume shape and path of the lumen and its attenuation density from start to finish of the lumen.

[0065] 6. The total number of plaques and plaque types before and after any given plaque to further inform its risk.

[0066] 7. Determining “higher plaque risk” by “subtracting” calcified (high density) plaques to get a better absolute measure of high risk plaques (low density plaques). In other words, this particular embodiment involves identifying calcified plaques and excluding them from further analysis of plaques for the purpose of identifying high risk plaques.

[0067] The metrics listed above and others can be analyzed together to assess the risk of a plaque in future heart attack, ACS, ischemia, or death. This can be done by developing and / or validating a traditional risk score or by a machine learning approach. Factors from the metric analysis that can be associated with heart attack, ACS, ischemia, or death can include: (1) the ratio of [bright lumen: dark plaque]; (2) the ratio of [dark plaque: light fat]; (3) the ratio of [bright lumen: dark plaque: light fat]; (4) the low ratio of [dark lumen: dark myocardium in one vessel site] / [lumen: myocardium in another vessel site]. Some improvements of the disclosed methods and systems include: (1) using numerical values from the ratios of [lumen: plaque], [plaque: fat], and [lumen: plaque: fat] rather than using qualitative definitions of atherosclerotic features; (2) using scan-specific [lumen: plaque attenuation] ratios to characterize plaques; (3) using scan-specific [plaque: fat attenuation] ratios to characterize plaques; (4) using the ratio of [lumen: plaque: fat perimeter] to characterize plaques; and (5) integration of plaque volume and type before and after as a risk factor for any given individual plaque.

[0068] Atherosclerotic plaque features can change over time with medical therapy (colchicine and statins), although some of these drugs can delay plaque progression, they also have a very important role in promoting plaque changes. While statins can reduce the overall progression of plaques, they can actually also cause an increase in progression of calcified plaques and a reduction in non-calcified plaques. This change will be associated with a reduction in heart attack or ACS or death, and the disclosed methods can be used to monitor the impact of medical therapy on plaque risk over time. In addition, the methods can also be used to identify individuals whose atherosclerotic plaque features or [lumen: plaque] / [plaque: fat] / [lumen: plaque: fat] ratios indicate that they are susceptible to rapid progression or malignant transformation of their disease. In addition, these methods can be applied to individual plaques or on a patient basis, where a global atherosclerosis tracking can be used to monitor the risk of a patient experiencing a heart attack (rather than trying to identify any particular plaque as the cause of a future heart attack). Tracking can be done through an automated registration process of image data associated with a patient over a period of time.

[0069] Figure 1A diagram depicting an example of an embodiment of a system 100 including a processing system 120 configured to characterize coronary artery plaque is depicted. 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 the one or more processor data communication interfaces that configure the one or more processors to perform a method of analyzing image information. A more detailed example of the server / computer 105 is described with reference to FIG. 4.

[0070] The system 100 also includes a network. The processing system 120 is in communication with the network 125. As at least a portion of the network 125, the network 125 can include the Internet, a wide area network (WAN), a wireless network, etc. In some embodiments, the processing system 120 is part of a “cloud” implementation that can be located anywhere in communication with the network 125. In some embodiments, the processing system 120 is located in the same geographic proximity as an imaging facility that images and stores patient image data. In other embodiments, the processing system 120 is located remotely from where patient image data is generated or stored.

[0071] Figure 1 Also shown in the system 100 are various computer systems and devices 130 (e.g., of an imaging facility) that are related to generating patient image data and that are also connected to the network 125. One or more of the devices 130 can be located at an imaging facility that generates patient arterial images, a medical facility (e.g., a hospital, a doctor’s office, etc.), or can be a personal computing device of a patient or a care provider. For example, as shown, an imaging facility server (or computer) 130A can be connected to the network 125. In addition, in this example, a scanner 130B in the imaging facility can be connected to the network 125. One or more other computer devices can also be connected to the network 125. For example, a laptop computer 130C, a personal computer 130D, and / or an image information storage system 130E can also be connected to the network 125 and in communication with the processing system 120 and each other via the network 125. Figure 1

[0072] ​Information communicated from devices 130 to processing system 120 via network 125 can include image information 135. In various embodiments, image information 135 can include 2D or 3D image data of a patient, scan information related to the image data, patient information, and other image or image related information related to the patient. For example, the image information can include patient information including a characteristic(s) of the patient, such as age, gender, body mass index (BMI), medication, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, body habitus (e.g., "body type" or "body shape" that can be based on a number of factors), medical history, diabetes, hypertension, prior coronary artery disease (CAD), dietary habits, medication history, family history of disease, information related to other previously collected image information, exercise habits, alcohol consumption habits, lifestyle information, laboratory results, etc. One example of a set of patient information is shown in Table 1300 in Figure 13 some embodiments, the image information includes identifying information of the patient, such as the patient's name, patient address, driver's license number, social security number, or another indicia of patient identity. Once processing system 120 analyzes image information 135, information related to patient 140 can be communicated from processing system 120 to devices 130 via network 125. Patient information 140 can include, for example, a patient report. In addition, patient information 140 can include various patient information available from a patient portal that can be accessed by one of devices 130.

[0073] In some embodiments, image information including a plurality of images of a patient's coronary arteries and patient information / characteristics can be provided to one or more servers 105 of processing system 120 from one or more of devices 130 via network 125. Processing system 120 is configured to generate coronary artery information using the plurality of images of the patient's coronary arteries to generate a two-dimensional and / or three-dimensional data representation of the patient's coronary arteries. Processing system 120 then analyzes the data representation to generate a patient report that records the patient's health status and risk related to coronary plaque. The patient report can include an image and graphical depiction of the patient's arteries in or near the coronary plaque types in the coronary arteries. Using machine learning techniques or other artificial intelligence techniques, the data representation of the patient's coronary arteries can be compared to data representations of other patients (e.g., data representations of patients stored in a database) to determine additional information about the patient's health. For example, based on certain plaque conditions of the patient's coronary arteries, it can be determined that the patient has a likelihood of a heart attack or other adverse coronary effects. In addition, for example, additional information about the patient's CAD risk can also be determined.

[0074] Figure 2is a schematic diagram illustrating an example of myocardium 225 and its coronary arteries. The coronary vasculature includes a complex network of blood vessels ranging from large arteries to small arteries, capillaries, venules, veins, etc. Figure 1 A model of the portion of the coronary vasculature that circulates blood to and within the heart, and including the aorta 240, which supplies blood to a plurality of coronary arteries, e.g., the left anterior descending (LAD) artery 215, the left circumflex (LCX) artery 220, and the right coronary (RCA) artery 205, as described further below. The coronary arteries supply blood to the myocardium 225. Like all other tissues in the body, the myocardium 225 needs oxygen-rich blood to function. In addition, oxygen-depleted blood must be carried away. The coronary arteries encircle the outside of the myocardium 225. Small branches penetrate into the myocardium 225 to bring it blood. Examples of the methods and systems described herein can be used to determine information related to blood flowing in the coronary arteries from any of the blood vessels extending therefrom. In particular, examples of the described methods and systems can be used to determine various information related to one or more portions of the coronary arteries in which a plaque has formed, which information is then used to determine a risk associated with such a plaque, e.g., whether the plaque forms a risk of an adverse event to the patient.

[0075] The right side 230 of the myocardium 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 that extends from the aorta 240 down the right side 230 of the myocardium 225, and the left main coronary artery (LMCA) 210 that extends from the aorta 240 down the left side 235 of the myocardium 225. The RCA 205 supplies blood to the right ventricle, the right atrium, and the SA (sinoatrial) and AV (atrioventricular) nodes that regulate the heart rhythm. The RCA 205 branches into smaller branches, including the right posterior descending artery and acute marginal arteries. The RCA 205, along with the left anterior descending artery 215, help supply blood to the middle or septum of the heart.

[0076] The LMCA 210 branches into two arteries: the anterior interventricular branch of the left coronary artery, also known as the left anterior descending (LAD) artery 215; and the circumflex branch of the left coronary artery 220. The LAD artery 215 supplies blood to the front of the left side of the heart. Occlusion of the LAD artery 215 is commonly known as the widowmaker infarction. The circumflex branch of the left coronary artery 220 encircles the myocardium. The circumflex branch of the left coronary artery 220 supplies blood to the lateral and posterior portions of the heart, along the left portion of the coronary sulcus, first to the left and then to the right, almost to the posterior longitudinal sulcus.

[0077] Figure 3The illustration shows an example of a set of images generated from a scan along the coronary arteries, including selected images of a portion of the coronary arteries, and how the image data corresponds to values ​​on the Henry scale. See reference... Figure 1 In addition to acquiring image data, the discussion also includes collecting scan information, including metrics related to the image data, as well as patient information, including patient characteristics.

[0078] Parts of myocardial 225, LMCA 210 and LAD artery 215 are in Figure 3 The example illustration shows that a set of images 305 can be collected along a portion of the LMCA 210 and 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, image data can be acquired using non-invasive imaging methods. For example, a scanner can be used to generate CCTA image data to create images of the heart and other vessels extending from it in the coronary arteries. The collected CCTA image data can then be used to generate a three-dimensional image model of the features contained in the CCTA image data (e.g., the right coronary artery 205, the left main coronary artery 210, the left anterior descending artery 215, the circumflex branch of the left coronary artery 220, the aorta 240, and other heart-related vessels appearing in the image).

[0079] 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 involves using a contrast agent to help identify the structures of the coronary arteries, the contrast agent being injected into the patient prior to the imaging procedure. Various imaging methods may have their own advantages and disadvantages in use, including resolution and applicability for imaging the coronary arteries. Imaging methods that can be used to collect coronary artery image data are constantly being improved as hardware (e.g., sensors and transmitters) and software are improved. The disclosed systems and methods contemplate using CCTA image data and / or any other type of image data that can provide or be converted into a representative 3D depiction of the coronary arteries, the plaques contained within the coronary arteries, and the perivascular fat located near the plaque-containing coronary arteries, such that attenuation or radiodensity values ​​of the coronary arteries, plaques, and / or perivascular fat can be acquired.

[0080] Still referencing Figure 3particular image 310 of the image data 305, which represents an image of a portion of the left anterior descending artery 215. The image 310 includes image information, the smallest points of which are manipulated by the system are generally referred to herein as pixels, such as pixel 315 of the image 310. The resolution of the imaging system used to capture the image data will affect the size of the smallest features that can be discerned in the image. Further, subsequent manipulation of the image can affect the size of the pixels. As one example, the image 310 in digital format can contain 4000 pixels in each horizontal row and 3000 pixels in each vertical column. The pixel 315, as well as each of the pixels in the image data 310 and in the image data 305, can be associated with a radiodensity value that corresponds to the pixel density in the image. Figure 3 A point is illustratively shown that maps the pixel 315 to the Hounsfield scale 320. The Hounsfield scale 320 is a scale used to describe the quantification of radiodensity. The Hounsfield unit scale linearly transforms the raw linear attenuation coefficient measurement to 1, where the radiodensity of distilled water at standard pressure and temperature is defined as zero Hounsfield Units (HU), and the radiodensity of air at standard pressure and temperature is defined as -1000 HU. Although Figure 3 The example of mapping the pixel 315 of the image 310 to the point on the Hounsfield scale 320 is illustrated, but such an association of pixels to radiodensity values can also be done with 3D data. For example, after the image data 305 is used to generate a three-dimensional representation of the coronary arteries.

[0081] Once the data has been acquired and rendered into a three-dimensional representation, various processes can be performed on the data to identify portions of the analysis. For example, the three-dimensional depiction of the coronary arteries can be segmented to define multiple portions of the arteries and so identified in the data. In some embodiments, the data can be filtered (e.g., smoothed) by various methods to remove anomalies or other various errors as a result of the scan. Various known methods for segmenting and smoothing 3D data can be used, and thus will not be discussed in further detail herein for the sake of brevity of the disclosure.

[0082] Figure 4A is a block diagram that illustrates a computer system 400 upon which various embodiments can 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 bus 402 for processing information. The hardware processor(s) 404 can be, for example, one or more general purpose microprocessors.

[0083] Computer system 400 also includes a main memory 406, such as a random access memory (RAM), cache and / or other dynamic storage devices, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions can be stored in one or more storage devices in processor 404 accessible by processor 404, such as within a memory controller, or in a chipset that is distinct from Processor 404, such as within a northbridge or southbridge of a computer system. The instructions can be those specifically designed and constructed for the purposes of the present disclosure, or they can be by way of background, techniques previously well known to those of ordinary skill in the art, which have been developed for the practice of such techniques by others. The computer system 400 also includes a read only memory (ROM) 408 or other static storage device coupled to the 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), etc., is provided and coupled to bus 402 for storing information and instructions.

[0084] The computer system 400 further includes a read only memory (ROM) 408 or other static storage device coupled to the 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), etc., is provided and coupled to bus 402 for storing information and instructions.

[0085] The computer system 400 can be coupled via the bus 402 to a display 412, such as a cathode ray tube (CRT) or LCD (liquid crystal display) monitor (or touchscreen), 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 processor 404. Another type of user input device is cursor control 416, such as a mouse, trackball, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), which allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections can be achieved via receiving touches on a touchscreen without a cursor.

[0086] The computing system 400 can include a user interface module for implementing a GUI, which can be stored in mass storage device as computer-executable program instructions executed by the computing device(s). As described below, the computer system 400 can also implement the techniques described herein using custom hard-wired logic, one or more ASICs or FPGAs, firmware and / or program logic which in combination with the computer system causes or programs the computer system 400 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by the computer system 400 in response to the processor(s) 404 executing one or more sequences of one or more computer-readable program instructions contained in the main memory 406. Such instructions can be read into the main memory 406 from another storage medium, such as a storage device 410. Execution of the sequences of instructions contained in the main memory 406 causes the processor(s) 404 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions.

[0087] Various forms of computer-readable storage media can 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 can initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can 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 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can 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 can optionally be stored on storage device 410 either before or after execution by the processor 404.

[0088] The 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 can be an integrated services digital network (ISDN) card, cable modem, 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 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to have a WAN communication). Wireless links can 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.

[0089] Network link 420 typically provides data communication through one or more networks to other data devices. For example, network link 420 can 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 in turn 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 and the signals on network link 420 and through communication interface 418, which carry the digital data to and from computer system 400, are example forms of transmission media for digital data.

[0090] 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.

[0091] The received code can be executed by processor 404 as it is received, and / or stored in storage device 410, or other non-volatile storage for later execution.

[0092] Thus, in one embodiment, computer system 105 includes a non-transitory computer storage medium storage device 410 configured to store at least image information of a patient. Computer system 105 can also include a non-transitory computer storage medium storage device storing instructions for one or more processors 404 to perform a process (e.g., 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 a coronary plaque and perivascular tissue adjacent to the coronary plaque. By executing the instructions, the one or more processors 404 can quantify a radiodensity in a region of the coronary plaque in the image data, quantify a radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data, determine a gradient of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue, determine a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue, and characterize the coronary plaque by analyzing one or more of the gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular tissue, or the ratio of the coronary plaque radiodensity value and the perivascular tissue radiodensity value.

[0093] Various embodiments of the disclosure can be a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. The computer readable program instructions can be executed by one or more hardware processors and / or any other suitable computing device. The software instructions and / or other executable code can be read from the computer readable storage medium (or media) by one or more hardware processors and / or any other suitable computing device.

[0094] The computer readable storage medium can be a tangible device that can retain and store data and / or instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a solid state drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0095] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0096] Computer readable program instructions for carrying out operations of the present disclosure (e.g., also referred to herein as "code", "instructions", "modules", "applications", "software applications", etc.) can be assembly-language instructions, instructions for an instruction-set architecture (ISA), machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or source code or object code written in any combination of one or more programming languages, including an object-oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can be invoked in response to detected events or interrupts, and / or can be invoked from other instructions or from themselves. Computer readable program instructions configured to execute on a computing device can be provided on a computer readable storage medium, and / or provided as a digital download (and can be initially stored in a compressed or installable format requiring installation, decompression, or decryption prior to execution), which can then be stored on a computer readable storage medium. Such computer readable program instructions can be stored, in whole or in part, on memory devices (e.g., computer readable storage media) of the computing device that executes them, to perform the operations of the computing device. Computer readable program instructions can execute entirely on a user's computer (e.g., the computing device of execution), partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0097] 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.

[0098] 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 produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including

[0099] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks. For example, the instructions can initially be carried on a disk or solid state drive of a remote computer. The remote computer can load the instructions and / or modules into its dynamic memory and transmit the instructions via a modem over a telephone, cable, or optical line. A modem local to the server computing system can receive the data on the telephone / cable / optical line and use a transceiver to convert the data to the bus for the server computing system. The bus carries the data to memory, from which the processor retrieves and executes the instructions. The instructions received by the memory can optionally be stored on a storage device, e.g., a solid state drive, either before or after execution by the computer processor.

[0100] The flow and block diagrams in the drawings show architectural, functional, and operational aspects 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 flowchart or block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions (s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. Also, a number of these blocks can be optional. The methods and processes described herein are also not limited to any particular order or sequence, and the blocks or states relating thereto can be performed in other suitable orders.

[0101] It should also be noted 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 special purpose hardware-based systems that perform the specified functions or acts or combinations of special purpose hardware and computer instructions. For example, any of the processes, methods, algorithms, elements, blocks, applications, or other functionality (or portions thereof) described in the foregoing sections can be embodied in electronic hardware and / or via electronic hardware completely or partially automated, such as an application-specific processor (e.g., an application-specific integrated circuit (ASIC)), a programmable processor (e.g., a field-programmable gate array (FPGA)), a special purpose circuit, etc. (any of which can also incorporate custom hardwired logic, logic circuits, ASICs, FPGAs, etc. that utilize software instructions to achieve the techniques, in conjunction with the custom programming / software instructions).

[0102] Any of the aforementioned processors and / or devices incorporating any of the aforementioned processors can be referred to herein as, for example, a “computer,” “computer device,” “computing device,” “hardware computing device,” “hardware processor,” “processing unit,” etc. The computing devices of the aforementioned embodiments are generally (but not exclusively) controlled and / or coordinated by operating system software such as: Mac OS, iOS, Android, Chrome OS, Windows OS (e.g., Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, Windows Server, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS, VxWorks, or other suitable operating system. In other embodiments, the computing devices can be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system and networking, I / O services, and provide user interface functionality, such as a graphical user interface (“GUI”), etc.

[0103] Figure 4B is a block diagram illustrating an example of representative instructions that can be executed by one or more computer hardware processors in one or more of the representative processing systems (computer systems) 120 that can implement various embodiments described herein. As shown, the processing system 120 can be implemented in one computer (e.g., a server) or in two or more computers (two or more servers). Although the instructions are represented in seven modules 450, 455, 460, 465, 470, 475, 480, in various implementations, the executable instructions can be in fewer modules (including a single module) or more modules. Figure 1 As shown, the processing system 120 can be implemented in one computer (e.g., a server) or in two or more computers (two or more servers). Although the instructions are represented in seven modules 450, 455, 460, 465, 470, 475, 480, in various implementations, the executable instructions can be in fewer modules (including a single module) or more modules. Figure 4B As shown, the processing system 120 can be implemented in one computer (e.g., a server) or in two or more computers (two or more servers). Although the instructions are represented in seven modules 450, 455, 460, 465, 470, 475, 480, in various implementations, the executable instructions can be in fewer modules (including a single module) or more modules. As shown, the processing system 120 can be implemented in one computer (e.g., a server) or in two or more computers (two or more servers). Although the instructions are represented in seven modules 450, 455, 460, 465, 470, 475, 480, in various implementations, the executable instructions can be in fewer modules (including a single module) or more modules.

[0104] The processing system 120 includes image information stored on the storage device 410, which can be from Figure 1 the network 125. The image information can include image data, scan information, and / or patient data. In this example, the storage device 410 also includes stored plaque information for other patients. For example, the stored plaque information for other patients can be stored in a database on the storage device 410. In other examples, the stored plaque information for other patients is stored on a storage device in communication with the processing system 120. The stored plaque information for other patients can be a collection of information from one, tens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of more patients.

[0105] The information for each patient can include a characterization of the patient's plaque, such as the density and density gradient of the patient's plaque, and the location of the plaque relative to the perivascular tissue near or adjacent to the plaque. The information for each patient can include patient information as shown in Figure 13 For example, this information can include one or more of: gender, age, BMI (body mass index), medication, blood pressure, heart rate, weight, height, race, body habitus, smoking history, diabetes history or diagnosis, hypertension history or diagnosis, prior coronary artery disease, family history of coronary artery disease and / or other diseases, or one or more laboratory results (e.g., blood work results). The information for each patient can include scan information as shown in Figure 14 For example, this information can include one or more of: 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, vasodilator, beta blocker, whether it is iterative or filtered back projection for scout options, whether it is standard resolution or high resolution for scout type, display field of view, rotation speed, whether it is perspective triggered or retrospective gated for gating, stent, heart rate, or blood pressure. The information for each patient can also include cardiac information as shown in Figure 15 For example, this information can include a characterization of the plaque including one or more of: density, volume, geometry (shape), location, remodeling, baseline anatomy (for diameter, length), compartment (internal, external, within), stenosis (diameter, area), myocardial mass, plaque volume and / or plaque composition, texture, or homogeneity.

[0106] The processing system 120 also includes memory 406, 408, which can be a main memory or a read-only memory (ROM) of the processing system. The memory 406, 408 stores instructions executable by one or more computer hardware processors 404 (which group is referred to herein as the "module") to characterize a coronary artery plaque. For brevity, with reference to this figure, the memory 406, 408 will be referred to collectively as the memory 406. Examples of functionality performed by the executable instructions are described below.

[0107] The memory 406 includes a module 450 that generates a 2-D or 3-D representation of the coronary artery, including the plaque and the perivascular tissue adjacent to or in the vicinity of the coronary artery in the plaque, from the image data stored on the storage device 410. The generation of the 2-D or 3-D representation of the coronary artery can be accomplished from the series of images 305 (e.g., CCTA images) described above with reference to Figure 3 Once the representation of the coronary artery is generated, different portions or segments of the coronary artery can be identified for evaluation. For example, based on input from a user, or based on features determined from the representation of the coronary artery (plaque), a portion of interest of the right coronary artery 205, the left anterior descending east branch 215, or the circumflex branch 220 of the left coronary artery can be identified as the site of analysis (site of interest).

[0108] In module 460, the one or more computer hardware processors quantify the radiodensity in the region of the coronary artery plaque. For example, the radiodensity in the region of the coronary artery plaque is set to a value on the Hounsfield scale. In module 465, the one or more computer hardware processors quantify the radiodensity of the perivascular tissue adjacent to the coronary artery plaque, and quantify the radiodensity value of the vessel lumen of interest. In module 470, the one or more computer hardware processors determine a gradient of the radiodensity values of the plaque, the perivascular tissue, and / or the lumen. In module 475, the one or more computer hardware processors determine one or more ratios of the radiodensity values in the plaque, the perivascular tissue, and / or the lumen. Next, in module 480, the one or more computer hardware processors use the gradient of the plaque, the perivascular tissue, and / or the lumen to characterize the coronary artery plaque, and / or to characterize the coronary artery plaque in relation to the perivascular tissue and / or the lumen, including comparing the gradient and / or the ratios to a database containing information of other patient plaque gradients and ratios. For example, the gradient and / or the ratios are compared to patient data stored on the storage device 410. Reference is made to Figures 6-12 Determining the gradient and the ratios of the plaque, the perivascular tissue, and the lumen are described in more detail.

[0109] Figure 5AAn example of a flowchart illustrating process 500 for analyzing a coronary artery plaque is shown. At block 505, process 500 generates image information including image data related to a coronary artery. In various embodiments, this can be accomplished by scanner 130B( Figure 1 ) receiving the image information via network 125( Figure 1 ). At block 515, process 500 generates, on the processing system, a 3D representation of the coronary artery including perivascular fat and plaque. The functionality of blocks 505, 510, and 515 can be performed, for example, using various scanning techniques for generating the image data (e.g., CCTA), communication techniques for transmitting the data over the network, and processing techniques for generating the 3D representation of the coronary artery from the image data.

[0110] At block 520, the processing system performs portions of process 500 to analyze the coronary artery plaque, which will be described in further detail with reference to process 550 of Figure 5B . Additional details of this process for analyzing the coronary artery plaque are described with particular reference to Figures 6-12 .

[0111] Figure 5B An example of a flowchart illustrating a portion of the flowchart in Figure 5A for determining characteristics of the coronary artery plaque is shown. Referring now to Figure 5B , at block 555, process 550 can utilize one or more processors 404 to quantify a radiodensity in a region of the coronary artery plaque. At block 560, process 550 can utilize one or more processors 404 to quantify a radiodensity in at least one region of corresponding perivascular tissue in the image data, by which is meant perivascular tissue adjacent to the coronary artery plaque. At block 565, process 550 determines a gradient of the quantified radiodensity values within the coronary artery plaque and the quantified radiodensity values within the corresponding perivascular tissue. The one or more processors 404 can be means for determining these gradients. At block 570, process 550 can determine a ratio of the quantified radiodensity values within the coronary artery plaque and the corresponding perivascular tissue. For example, perivascular tissue adjacent to the coronary artery plaque. The one or more processors 404 can determine these ratios. At block 575, process 550 can utilize one or more processors 404 to characterize the coronary artery plaque by analyzing one or more of: the quantified radiodensity values in the coronary artery plaque and the corresponding perivascular tissue, or the ratio of the coronary artery plaque radiodensity values and the corresponding perivascular tissue radiodensity values. Process 550 can then return to process 500, as indicated by circle A.

[0112] Referring again to Figure 5AIn box 525, process 500 can compare identified information about coronary artery plaques in a specific patient with stored patient data (e.g., patient data stored on storage device 410). Figure 15 The diagram illustrates an example of coronary artery plaque information for a specific patient that can be compared to stored patient data. To better understand coronary artery plaque information for a specific patient, and / or to help identify coronary artery plaque information for a specific patient, one can use... Figure 14 One or more scan information from the scan information shown. Furthermore, when comparing coronary plaque information for a specific patient with previously stored coronary plaque information, one or more characteristics of the patient can be compared, including, for example... Figure 13 One or more of the patient's characteristics are shown. In some examples, the coronary plaque information of a specific patient being examined can be compared to, or analyzed with reference to, patients with one or more of the same or similar patient characteristics. For example, the patient being examined can be compared to patients with the same or similar characteristics as: sex, age, BMI, medication, blood pressure, heart rate, weight, height, race, physical habits, smoking, diabetes, hypertension, previous coronary artery disease, family history, and laboratory results. Such comparisons can be made using various means, such as machine learning and / or artificial intelligence techniques. In some examples, neural networks are used to compare a patient's coronary artery information with that of numerous (e.g., 10,000+) other patients. For such patients with similar patient information and similar cardiac information (e.g., Figure 15 For patients with the characteristics shown, the risk assessment of the plaque in the examined patient can be determined.

[0113] Figure 6 The illustration shows a representation of image data depicting an example portion of the coronary artery 665 (sometimes referred to as a "vessel" here for ease of reference). Although Figure 6 It is a two-dimensional (2D) illustration, but the image data being analyzed can be two-dimensional or three-dimensional (e.g., volume). Figure 6 The illustration also shows an example of a plaque located in blood vessel 665 and perivascular fat located adjacent to blood vessel 665. Figure 6 Further illustration shows a region that may include blood vessel 665, plaque within blood vessel 665, or at least a portion of perivascular fat adjacent to blood vessel 665 (in Figure 6 Examples are shown in rectangles, where these regions indicate portions of one or more of the vessel 665, plaque, or perivascular fat, which can be analyzed to determine the density, density gradient, and / or density ratio of the vessel 665, plaque, or perivascular fat to determine one or more characteristics of the patient's coronary arteries. Figure 6As shown, the blood vessel 665 includes blood vessel walls 661 and 663 depicted as boundary lines to provide a graphical reference for the location of the plaque and fat in the Figure 6 The blood vessel walls 661 and 663 can sometimes be referred to herein as a first blood vessel wall and a second blood vessel wall, or vice versa. The lines delineating the blood vessel walls 661, 663 represent the outer boundaries of the blood vessel walls 661, 663. In the example shown, all of the fat 625, 640 is located outside of the blood vessel walls 661, 663, and all of the plaque in the plaques 610, 620, 635, 650 is located inside of the blood vessel walls 661, 663. Figure 6

[0114] Plaque can be characterized by its attenuation as exhibited in a coronary artery image. For example, plaque can be characterized as low attenuation plaque, moderate attenuation plaque, high attenuation plaque, or very high attenuation plaque. In some cases, these characteristics are not entirely accurate and can be influenced by the method and process used to collect the coronary artery image. In some examples, low attenuation plaque can have a density of about 0 to about 70. In some examples, moderate attenuation plaque can have a density of about 70 to about 350. In some examples, high attenuation plaque can have a density of about 350 to about 1000. In some examples, very high attenuation plaque can have a density of over 1000.

[0115] Figure 6 An example of different types of plaque that can be contained within the blood vessel walls 661 and 663 of the blood vessel 665 is shown in accordance with some embodiments. In one example, the plaque can be a fibrous plaque 610 having moderate attenuation characteristics, inside the blood vessel wall 663, and extending toward the interior of the blood vessel 665. In this example, the fibrous plaque 610 has other types of plaque walls 663 adjacent thereto within the blood vessel, and fat distributed outside of the blood vessel 665 and juxtaposed or adjacent to the fibrous plaque 610. As shown, distributed adjacent to the fibrous plaque 610 is a necrotic core plaque 615 having low attenuation characteristics. Figure 6 As shown, distributed adjacent to the fibrous plaque 610 is a necrotic core plaque 615 having low attenuation characteristics. Figure 6 Also illustrated is an example of a plaque 620 having moderate to high attenuation characteristics, also distributed (or located) adjacent to the fibrous plaque 610. In this example, the plaque 620 is also distributed adjacent to the necrotic core plaque 615, such that the plaque 620 is at least partially between the plaque fibrous 610 and the necrotic core plaque 615 and the outer boundary of the blood vessel wall 663. Figure 6 Also shown is an example of a very high attenuation plaque 635, distributed within the blood vessel wall 663, but protruding out of the blood vessel 665, such that the blood vessel wall 663 extends outward (i.e., away from the center of the blood vessel 665) around the plaque 635. In another example, the plaque 635 is distributed within the blood vessel wall 663, but protrudes out of the blood vessel 665, such that the blood vessel wall 663 extends outward around the plaque 635. Figure 6 ​​A fibrous plaque 650 is illustrated that has moderate attenuation properties (i.e., the attenuation properties are not as high as the fibrous plaque 610 that is distributed adjacent to and within the vessel wall 661). As shown, the fibrous plaque 650 generally extends toward the center of the vessel 665. Figure 6

[0116] Figure 6 Further illustrated are examples of fat that is outside of the vessel and adjacent to (or at least near) the vessel 665. Figure 6 The fat shown is also near and / or adjacent to one or more of the plaques 610, 615, 620, 635. In one example, the fat 625 is along a portion of the vessel wall 623, adjacent to the vessel wall 663, and collocated with the plaque 620 such that it is adjacent to the plaque 620 and near the plaque 610 and the plaque 615. In another example, the fat 630 is shown outside of the vessel wall 663 and adjacent to the plaque 620. In this example, the fat 630 at least partially surrounds a portion of the plaque 620 extending from the vessel 665 such that a portion of the fat 630 is adjacent to the vessel wall 663 on both sides of the plaque 620. Figure 6 In another example shown, the fat 640 is shown outside of the vessel wall 663 and adjacent to the plaque 635. In this example, the fat 640 at least partially surrounds a portion of the plaque 635 extending from the vessel 665 such that a portion of the fat 640 is adjacent to the vessel wall 663 on both sides or more of the plaque 635.

[0117] Identifying high-risk plaques can depend on the interaction between contrast attenuation within the coronary lumen, the attenuation pattern of the plaque, and the attenuation pattern of the fat. As described above, Figure 6 Also illustrated are boxes indicating examples of portions of the vessel 665, the perivascular fat, and / or the plaque that include analysis for density gradient and density ratio. In Figure 7 The boxes shown, and Figure 11 , 10 , 11, and 12, the boxes are shown as two-dimensional rectangles, and the overlaid portion of the representation of the image data depicts a portion of the coronary artery (vessel 665), the plaque, and the fat. As will be described below with respect to Figure 6 and 12 ​In some examples, the portion of image data analyzed can be a one-dimensional vector of data representing pixels in a rectangular box. In other words, image data along a line in the rectangular box. In other examples, the portion of image data analyzed can be a two-dimensional vector of pixels in a rectangular box. In other words, image data in two or more adjacent rows contained in the rectangular box. In some cases, for a two-dimensional vector of pixels, the image data in two or more adjacent rows can be processed to form a one-dimensional vector. For example, the image data in the two or more adjacent rows of image data can be averaged, which helps to reduce the impact of noise. In certain cases, the image data can be filtered to reduce the impact of noise. In some cases, filtering can occur prior to analyzing the image data for gradients, ratios, slopes, minimum density, maximum density, etc.

[0118] Figure 6 An example of a region indicated by box 605 is illustrated, where the contrast decay pattern in a proximal portion of the coronary artery lumen can be analyzed, box 605 extending from a central region of the vessel 665 toward the vessel wall 661. Figure 6 Another example of a region indicated by box 652 is illustrated, where the contrast decay pattern in a portion of the coronary artery lumen of the vessel 665 can be analyzed, box 652 extending longitudinally relative to the vessel 665 from a central region of the vessel 665 toward the vessel wall 661. Figure 6 A further example of a region indicated by box 662 is illustrated, where the contrast decay pattern in a portion of the lumen, a portion of the fibrous plaque 610, and a portion of the plaque 620 can be analyzed, box 662 thus covering a portion of the vessel 665 and portions of the fibrous plaque 610 and the plaque 620. Figures 9-12Further examples of analysis of attenuation patterns in the attenuation patterns of the coronary artery lumen and the attenuation pattern of the fat adjacent to the plaque 635 are illustrated by block 642, which extends over portions of the plaque 635 and portions of the fat 640 adjacent to the plaque 635. Information determined by analyzing various aspects of the density of the coronary artery features (e.g., lumen, plaque, and / or perivascular fat) can be combined with other information to determine characteristics of the patient's artery. In some examples, for any of the lumen, plaque, or perivascular fat, the determined information can include one or more of the following: a slope / gradient of the feature, a maximum density, a minimum density, a ratio of the slope of the density of one feature to the slope of the density of another feature, a ratio of the maximum density of one feature to the maximum density of another feature, a ratio of the minimum density of a feature to the minimum density of the same feature, a directionality of the density ratio (e.g., a density ratio between features facing one passageway or direction and features facing the opposite direction (e.g., a radiodensity ratio of features facing inward toward the myocardium and features facing outward toward the pericardium)), or a ratio of the minimum density of a feature to the maximum density of another feature. Such determined information can be indicative of a significant difference in plaque risk in the patient. In some examples, the determined information (e.g., as listed above) can be used with a percent diameter of stenosis to determine characteristics of the patient's artery. With reference to Figure 6 Additional information is described regarding examples of analysis of attenuation patterns in the attenuation patterns of the coronary artery lumen and the attenuation pattern of the fat.

[0119] Still referring to Figures 7-12 In examples of directionality of radiodensity ratios, the density of portions of the necrotic core plaque 615 to the density of portions of the vessel 665 (e.g., plaque: vessel facing inward ratio) can be determined and can be indicative of a certain plaque risk. In another example of directionality of radiodensity ratios, the density of portions of the vessel 665 to the density of the necrotic core plaque 615 (e.g., vessel: plaque facing outward) can be determined and can be indicative of a certain plaque risk. In another example, a density ratio of the necrotic core plaque 615 to the density of portions of the vessel 665 (e.g., plaque: vessel facing inward ratio) can be compared to a density ratio of the necrotic core plaque 615 to the fibrous plaque 620 (e.g., plaque: plaque facing outward) can be indicative of a certain plaque risk. In other examples, adjacently positioned features can be used to determine radiodensity values of inward and / or outward directionality that can be used to indicate risk associated with the plaque. Such ratios can provide a significant difference in plaque risk. Various embodiments of directionality radiodensity values and / or directionality radiodensity ratios can be included with any other information described herein to indicate plaque risk.

[0120] The size of the compartment can also be used to indicate risk associated with the plaque. For example, the determination of risk associated with the plaque can be based at least in part on the size of the compartment, such that the ratio of radiodensities influences the determination of risk and a function of the size of the compartment can also influence the determination of risk. While there is a plaque in the patient where the ratio of plaque: fat can indicate a high risk plaque, if there is only a small amount of plaque (e.g., plaque in a small compartment), it can be less risky than if there is a larger compartment with the same ratio of radiodensities of plaque to fat. In one implementation, the size (e.g., volume) of the compartment features (e.g., lumen, plaque, perivascular tissue (fat), and myocardium) can be determined, and the ratio of radiodensities can also be determined, and then the ratio can be weighted based on the size of the compartment. For example, a large compartment can increase the weight of the ratio to make the ratio more indicative of risk associated with the plaque. Similarly, a small compartment can decrease the weight of the ratio to make the ratio less indicative of risk associated with the plaque. In one implementation, only the compartment size of the plaque is used to weight (or adjust) the ratio. In one implementation, the compartment size of both features used in the ratio of radiodensities can be used to weight the ratio to determine the resulting risk. In one implementation, the compartment size of one of plaque, lumen, perivascular tissue, or myocardium is used to weight (or adjust) the risk associated with the ratio of radiodensities. In one implementation, the compartment size of more than one of plaque, lumen, perivascular tissue, or myocardium is used to weight the risk associated with the ratio of radiodensities. Various embodiments of using compartment size to determine plaque risk can be included with any other information described herein to indicate plaque risk. The compartment size can be used to weight other information or otherwise adjust the risk associated with the ratio of radiodensities in the examples described with reference to Figures 16-19 and Figure 7 The compartment size can be used to weight other information or otherwise adjust the risk associated with the ratio of radiodensities in the examples described with reference to

[0121] Figure 6 FIGURE 1 illustrates the same features of the vessel 665 and plaque and fat as shown in Figure 6 FIGURE 1, and further illustrates additional examples of sites of the artery, as well as plaque and / or perivascular fat near the artery, that can be analyzed to determine the patient's arterial properties. Similar to the illustration in Figure 7 FIGURE 1, such sites are indicated by rectangular boxes in Figure 6 Although particular locations of the rectangular boxes are illustrated in Figure 7 and Figure 7 these are merely examples of sites that can be analyzed. In one example, Figure 7 FIGURE 1 illustrates a box 660 that includes a portion of the vessel 665, a portion of the necrotic core plaque 615, a portion of the fibrous plaque 610, a portion of the plaque 620, and a portion of the fat 625. In another example, Figure 7A box 655 is illustrated that includes a portion of the blood vessel 665, a portion of the fibrous plaque 610, a portion of the plaque 620, a portion of the necrotic core plaque 615, and a portion of the fat 625. In some cases, the box 655 can be illustrated as a general area for analysis due to the presence of 3 different types of plaque 610, 615, 620 and adjacently distributed fat 625. Specific portions of the general area for analysis can be analyzed to better understand the characteristics formed by the adjacent features. For example, Figure 7 A general area 665 is illustrated that includes the box 660 (as described above), a box 673 that extends through portions of the fibrous plaque 610 and the plaque 620, and a box 674 that extends through portions of the plaque 620 and the perivascular fat 625. As another example, Figure 7 Another box 672 is also shown that extends through portions of the blood vessel 655 and the necrotic core plaque 615. As a further example, Figure 8 A box 671 is illustrated that extends through portions of the blood vessel 665 and the fat 640 that is juxtaposed with the blood vessel 665. As a further example, Figure 9 A box 670 is illustrated that extends through portions of the blood vessel 665 and the plaque 635. Characteristics of the patient's artery that can be analyzed based on these features can include:

[0122] 1. The ratio of lumen attenuation to plaque attenuation, where the volume model of the scan-specific attenuation density gradient within the lumen is adjusted for reduced lumen density throughout the plaque lesions that are functionally more significant in terms of risk value.

[0123] 2. The ratio of plaque attenuation to fat attenuation, where plaques with high radiodensity are considered to be of lower risk, even in the subset of plaques considered to be "calcified", where

[0124] There can be a density gradient (e.g., 130 to 4000 HU) and risk is considered to decrease with increasing density.

[0125] 3. The ratio of lumen attenuation / plaque attenuation / fat attenuation

[0126] 4. The ratio of #1-3 as a function of the 3D shape of the atherosclerosis, where 3D texture analysis of the plaques can be included

[0127] 5. The 3D volume shape and path of the lumen and its attenuation density from start to finish of the lumen.

[0128] 6. The total number and type of plaques before and after any given plaque to further inform its risk.

[0129] 7. "Higher plaque risk" is determined by "subtracting" calcified (high-density) plaques to obtain a better absolute measurement of high-risk plaques (low-density plaques). In other words, this particular embodiment involves identifying calcified plaques and excluding them from further analysis of plaques used for the purpose of identifying high-risk plaques.

[0130] Figure 9 Examples of images of certain regions of the heart 800 and coronary arteries 805 are shown. In this example, at region 810, there is high contrast attenuation in the proximal portion of the vessel. At region 820, there is low contrast attenuation in the distal portion of the vessel. At region 830, there is low contrast attenuation in the myocardium, i.e., in the region of myocardium near the distal portion of the vessel. Radiodense values ​​in these regions can be determined and compared. In some examples, the ratio of radiodense values ​​of regions 830 and 820 (i.e., radiodense value 830:radiodense value 820) and / or the ratio of radiodense values ​​of region 830 to 810 (i.e., radiodense value 830:radiodense value 810) can be used to determine the presence of ischemia.

[0131] Figure 9 The illustration shows an example of an overview of the representation of image data of a coronary artery (vessel) 905. In this example, the vessel 905 includes a lumen wall 910, a lumen wall 911 (a line indicating the outer boundary of the lumen wall), and a plaque 915 within the vessel 905, i.e., the plaque 915 is within the lumen wall 910 and extends outward from the center of the vessel 905 and inward toward the center of the vessel 905. Figure 10 The illustration also shows perivascular fat 920 adjacent to plaque 915 and distributed outside blood vessel 905. That is, the luminal wall 910 lies between the perivascular fat 920 and plaque 915. The plaque 915 within the perivascular fat diet 920 all exhibit contrast attenuation patterns that can be analyzed to determine the characteristics of coronary artery 905. Figure 10 It also includes punctate calcifications 925 located within plaque 915. In this example, G1 represents the portion of perivascular fat 920 and plaque 915 extending from the inner surface 930 of plaque 915 to the outer surface 935 of perivascular fat 920, where the gradient of contrast attenuation density can be determined and evaluated, which is in Figure 9 A more detailed description is provided below.

[0132] Figure 9 The diagram shows... Figure 10 Another view of the image data of the coronary artery (vessel) 905 shown illustrates examples of certain features of plaque, perivascular tissue (e.g., fat), and lumen that can be evaluated to characterize coronary artery plaques in determining the health properties of a patient's artery. Figure 10 The plaque 915 and perivascular fat 920 shown are also...Figure 10 is shown. Figure 10 The image data in

[0133] The density of the image data in portions of the image data depicting the perivascular fat 920, the plaque 915, and / or the lumen of the vessel 905 can be evaluated to characterize the coronary plaque to determine the health characteristics of the patient's artery. As described above, the information determined by analyzing various aspects of the density of the lumen, the plaque, and / or the perivascular fat can include, but is not limited to, one or more of the following: the slope / gradient of a feature, the maximum density, the 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. Any of this information can be combined with other information used to determine the characteristics of the patient's artery.

[0134] Figure 10 Several examples of regions of the image data that can be evaluated are illustrated, other evaluation regions can also be selected in other examples. A first example is a region 931 of the perivascular fat 920 demarcated by a rectangular box indicating the perivascular fat 920. The region 931 extends from the edge of the perivascular fat region 920 to the plaque 915, and the image data evaluated can be one or more dimensions (e.g., two-dimensional). Another example is a region 941 that extends through the plaque 915. The region 941 is adjacent to the perivascular fat region 931 on one side, and adjacent to a lumen region 939 on the opposite side. The lumen region 939 extends from the plaque 915 through a portion of the vessel 905. In this configuration, the perivascular fat region 931, the plaque region 941, and the lumen region 939 align and span the lumen of the vessel 905, the plaque 915, and the perivascular fat region 920. The density of the image data in some or all of these regions is evaluated with respect to their maximum density, minimum density, gradient, or the ratio of one of these features, as described herein.

[0135] In another example, a plaque-lumen region 937 is demarcated by a rectangular box that extends from the lumen wall 911 through the vessel 905 and through the plaque 915. The plaque-lumen region 937 represents a two-dimensional density set of image data all or portions of which can be evaluated.

[0136] In another example, as Figure 11As shown, the perivascular fat-plaque region 933 is an evaluation region delineated by a holder box extending from the edge 930 of the plaque 915 to the edge 935 of the perivascular fat 920. This example illustrates that in some cases, two or more adjacent vectors (or "lines") of image data across a feature in image data can be evaluated, which can include one or more features (e.g., fat, plaque, lumen). Evaluation of two or more adjacent vectors of image data can yield a more robust measure that is less affected by noise in the image data.

[0137] In another example, the perivascular fat-plaque region 938 is delineated by a rectangular box extending from the edge 930 of the plaque 915 into the vessel 905 to the edge 935 reaching the perivascular fat 920 distal to the plaque 915. The perivascular fat-plaque region 938 represents a one-dimensional density set of image data that all or a portion of which can be evaluated. As Figure 11 As an example of a measure of a feature depicted in FIG. 6B (using only radiodensity values, for example), the gradient slope of the image density values in the plaque 915 in the perivascular fat-plaque region 938 can be -3, the maximum density of the plaque 915 can be 98, and the minimum density of the plaque 915 can be -100. The slope of the gradient of the perivascular fat 920 and the perivascular fat-plaque region 938 can be -5, the maximum density of the perivascular fat 920 in the perivascular fat-plaque region 938 can be 180, and the minimum density of the perivascular fat 920 in the perivascular fat-plaque region 938 can be 102. Other measures of the perivascular fat-plaque region 938 can include: the ratio of the slope of the plaque 915 to the perivascular fat 920 {-3 / -5}, the ratio of the maximum density of the plaque 915 to the maximum density of the perivascular fat 920 {98 / 180}; the ratio of the minimum density of the plaque 915 to the minimum density of the perivascular fat 920 {-100 / 102}; the ratio of the minimum density of the plaque 915 to the maximum density of the perivascular fat 920 {-100 / 180}; the ratio of the maximum density of the plaque 915 to the minimum density of the perivascular fat 920 {98 / 102}; and the gradient across the entire perivascular fat-plaque region 938 (e.g., -4).

[0138] Figure 10 FIG. 6B illustrates another example of determining radiodensity values of a region of perivascular fat and plaque to determine a measure, as described herein. Figure 11 A coronary artery 905, a coronary plaque 915 located in a lumen wall 910 of the coronary artery 905, and a perivascular fat 920 located outside the lumen wall 910 and adjacent to the coronary plaque 915 are shown, similar to Figure 11 as shown.

[0139] Figure 12 Further illustration shows an example where radiodensity data can be evaluated to characterize two regions of the plaque. These two regions include a first region (or perivascular fat region 1131) in perivascular fat 920 and a second region (or plaque region) 1141 adjacent to the first region 1131 and within the coronary artery plaque 915. In this example, the coronary artery 905 and the lumen wall 910 are depicted as being roughly vertically aligned on the page. The plaque 915 and fat 920 are shown extending laterally from the artery 905 (e.g., to the left relative to the orientation of the figure). The plaque 915 lies within the lumen wall 910 of the artery 905, such that the lateral extent of the lumen wall 910 is shown coinciding with the leftmost boundary 950 of the plaque 915. The dashed line 1125 indicates the alignment of the coronary artery 905 at this location and, in this example, indicates the centerline of the artery 905 aligned with the artery 905 at this location.

[0140] like Figure 10 As shown, fat region 1131 and plaque region 1141 represent locations where radiodensity information (e.g., 3D or 2D) from image data generated from one or more images is evaluated to characterize plaque 915. As described above, in some examples, one or more images can be used to generate a 3D dataset representing the coronary artery, plaque within the artery, and perivascular tissue located near or adjacent to the artery and / or plaque. Once the dataset is generated, it can be used to characterize the relationship between one or more of the plaque, perivascular tissue, and luminal tissue. In some embodiments of plaque evaluation, the dataset is used as a 3D dataset (which may also be referred to as a 3D model). In some embodiments of plaque evaluation, the dataset is used as a 2D dataset, where information in the dataset is viewed in an XY (2D) region. Figure 10 In the example shown, the image data can be presented as 2D or 3D data.

[0141] In some embodiments for plaque evaluation, regions of radiodensity values ​​along lines in the image data can be used, regardless of whether the data being evaluated is a 2D or 3D representation of an artery. These regions of radiodensity values ​​along lines can be referred to as “linear regions.” A linear region can indicate part or all of the data that includes or passes through one or more types of tissue (e.g., plaque, perivascular tissue, and / or the lumen of the coronary artery). That is, the region can be described as indicating a portion of one or more types of tissue (e.g., plaque, perivascular tissue, and / or the lumen of the coronary artery) that indicates some information in the dataset is included within a specific region. The radiodensity data within a linear region is a 1×n vector, where n represents the number of discrete points of radiodensity data along the vector. Figure 11The diagram illustrates and describes some examples of regions that can be linear regions. For example, the luminal region 939 includes a portion of the vascular tissue 905. Figure 11 The region includes a portion of coronary artery plaque 915, a portion of perivascular fat 920, a portion of perivascular fat-plaque region 935 including portions of perivascular fat 920 and plaque 915, and a plaque-lumen region 937 including portions of plaque 915 and blood vessel 905.

[0142] exist Figure 11 In the example shown, plaque region 1141 delineates a portion of plaque 915 and extends in a transverse (or substantially transverse) direction aligned with the coronary artery, as shown by the centerline 1125 of vessel 905. Plaque region 1141 extends from a proximal end 1115 closest to the center of artery 905 to a distal end 1120 extending transversely away from the centerline 1125 of artery 905. Perivascular fat region 931 extends from a proximal end 1105 closer to artery 905 to a distal end 1110 furthest from artery 905. Also as... Figure 12 As shown, the perivascular fat region 1131 is aligned (or substantially aligned) with the plaque region 1141. The proximal end 1105 of the perivascular fat region 1131 is close to or adjacent to the distal end 1120 of the plaque region 1141.

[0143] Radiodensity data in the perivascular fat region 931 is represented by radiodensity value 951. Radiodensity data in the plaque region 941 is represented by radiodensity value 961. As described above, these radiodensity values ​​951 and 961 can be expressed using the aforementioned Hounsfield units. Once regions 1131 and 1141 are determined, and the radiodensity values ​​within these regions are determined, the radiodensity value 951 representing the density of a portion of the perivascular fat 920 and the radiodensity value 961 representing the radiodensity of a portion of the plaque 915 can be analyzed to characterize the plaque and aid in assessing its risk.

[0144] Analysis of the radiodensity values ​​of each linear region can be performed to determine a metric indicating the radiodensity values ​​in the region, or the relationship between the cardiac density values ​​in one region and another region. A plaque can be characterized by analyzing one or more metrics determined by the maximum density, minimum density, and / or the slope of the gradient (sometimes simply referred to as "slope" for convenience) of one or more regions (e.g., adjacent regions). In some examples, determining the metric may include determining one or more of the maximum density, minimum density, and / or the slope of the gradient of the radiodensity values. One or more of the following: gradient of a feature, maximum density, minimum density, ratio of the slope of one feature to the slope of another feature, ratio of the maximum density of one feature to the maximum density of another feature, ratio of the minimum density of a feature to the minimum density of the same feature, or ratio of the minimum density of a feature to the maximum density of another feature.

[0145] In a specific example, reference Figure 11 Plaque 915 can be characterized by analyzing one or more of the maximum density, minimum density, and slope of the radiodensity values ​​in the perivascular fat region 1131 and the adjacent plaque region 1141. For example, in the illustrated example of radiodensity values ​​in regions 1131 and 1141, the maximum and minimum densities of the perivascular fat 920 are 120 and 34, respectively, and the maximum and minimum densities of plaque 915 are 30 and -79, respectively. The gradient of the radiodensity values ​​in plaque region 1141 is -2. The gradient of the cardiac density values ​​in the perivascular fat region 1131 is -3. The determined metrics may include, for example:

[0146] (a) Gradient of perivascular fat region 1131 and gradient of plaque region 1141

[0147] Ratio: {-2:-3};

[0148] (b) The maximum density of the perivascular fat region 1131 and the maximum density of the plaque region 1141

[0149] High density ratio: {120:30};

[0150] (c) The minimum density of the perivascular fat region 1131 and the minimum density of the plaque region 1141

[0151] The ratio of low density is: {34:-79};

[0152] (d) The minimum density of the perivascular fat region 1131 and the minimum density of the plaque region 1141

[0153] High density ratio: {34:30}

[0154] (e) the maximum density of the perivascular fat region 1131 to the minimum density of the plaque region 1141

[0155] the ratio of small to large densities: {120: -79}; and

[0156] (f) the gradient from the proximal end 1115 of the plaque region 1141 (e.g., the inner surface of the plaque 915) to the distal end 1110 of the perivascular fat region 1131 (e.g., the outer surface of the perivascular fat 920): -2.

[0157] According to various embodiments, the maximum or minimum density of the radiodensity values in a region can be determined in a variety of ways. In one example, the maximum / minimum radiodensity value can simply be selected as the maximum or minimum. However, some data sets can include outliers that indicate erroneous data. If it can be determined that an outlier radiodensity value is actually erroneous (e.g., using statistical methods), the outlier value can be removed from the analysis, or corrected if possible. Outlier values can be due to random variation or can indicate something scientifically interesting. Regardless, we generally do not want to simply remove outliers from our observations. However, if the data contains significant outliers, robust statistical techniques or alternative imaging techniques can be employed to filter the image data to improve the accuracy of the metrics.

[0158] Figure 12 A representation of image data is illustrated showing the coronary artery 905, plaque 915, and perivascular fat 920 adjacent to the plaque (as shown in FIG. 12A). Figure 11 Figure 13 Also illustrated are a perivascular fat region 1231 and the perivascular fat 920, and a plaque region 1241 in the plaque 915. The perivascular fat region 1231 and the plaque region 1241 are different from the perivascular fat region 1131 and the plaque region 1141 shown in FIG. 12A in that the perivascular fat region 1231 and the plaque region 1241 are defined by the user. Figure 14 ​The fat regions 1131 and plaque regions 1141 are shown as two-dimensional vectors of radiodensity values in these regions. That is, if each of the regions 1231, 1241 contains rows (e.g., transverse with respect to the page) and columns (e.g., vertical with respect to the page) of image data representing the heart in the regions 1231, 1241, then the density values in the regions 1231, 1241 include radiodensity values for two or more adjacent rows. In one example, the radiodensity values for the two or more adjacent rows can be used to generate minimum and maximum density values, for example, by taking the maximum and minimum density values from any of the two or more adjacent rows. In another example, the radiodensity values for the two or more adjacent rows can be used to generate minimum and maximum density values by averaging the information in the two or more rows, for example, by averaging the maximum radiodensity values in each of the rows to determine the maximum radiodensity value for the region, and by averaging the minimum radiodensity values in each of the rows to determine the minimum heart density value for the region. Similarly, a gradient of the radiodensity values in each of the regions can be calculated based on the radiodensity values in the two or more rows. For example, a gradient of the radiodensity values in each row of the region can be calculated, and the gradient can be determined by averaging each of the calculated gradient values. Other statistical techniques are contemplated for averaging multiple radiodensity values in a region to determine characteristics and metrics for the region. Such techniques can be particularly useful for minimizing the impact of noise (inaccurate data) in a region.

[0159] Figure 15 Table 1300 is an example of a table illustrating a set of patient information. In this example, table 1300 includes two columns: a first column 1305 labeled "item" and a second column 1310 labeled "importance or value."

[0160] In this example, the items of patient information in the first column 1305 include information for the patient's gender, age, BMI, medication, blood pressure, heart rate, weight, height, race, body habitus, smoking, diabetes, hypertension, prior CAD, family history, and lab. In other examples, more or fewer items can be included, and / or different items can be included.

[0161] The second column 1310 can include a numerical rating of the importance or value of each item in the first column 1305. The numerical ratings can be provided by a risk score and used to bias the analysis based on one or more items that are considered more important. In some examples, each item has the same assigned value. In other examples, one or more items can be assigned different values. In some examples, the values can be normalized to add up to 1.0, or 100%, or another value.

[0162] Figure 16 Table 1400 is an example of a set of scan information. Table 1400 includes a first column 1405 labeled "Item" listing scan-related items, and a second column 1410 labeled "Importance or Value." In this example, the items of scan information in the first column 1405 include 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, vasodilator, beta blocker, scout option of whether it is iterative or filtered back projection, scout type of whether it is standard resolution or high resolution, display field of view, rotation speed, gating of whether it is perspective triggered or retrospective gated, stent, heart rate, or blood pressure. In other examples, more or fewer items can be included, and / or different items can be included.

[0163] The second column 1410 can include a numerical rating of the importance or value of each item in the first column 1405. The numerical rating can be provided by a risk score and used to bias the analysis based on one or more items that are considered more important. In some examples, each item has the same assigned value. In other examples, one or more items can be assigned different values. In some examples, the values can be normalized to add up to 1.0, or 100%, or another value.

[0164] Figure 16 Table 1500 is an example of a set of cardiac information. Table 1500 includes a first column 1505 labeled "Item" listing scan-related items, and a second column 1510 labeled "Importance or Value." In this example, the items of cardiac information in the first column 1504 include contrast with density, volume, geometry-shape, location, remodeling, baseline anatomy (for diameter, length), compartments (internal, external, within), stenosis (diameter, area), myocardial mass, plaque volume, plaque composition, texture, or homogeneity. In other examples, more or fewer items can be included, and / or different items can be included.

[0165] The second column 1510 can include a numerical rating of the importance or value of each item in the first column 1505. The numerical rating can be provided by a risk score and used to bias the analysis based on one or more items that are considered more important. In some examples, each item has the same assigned value. In other examples, one or more items can be assigned different values. In some examples, the values can be normalized to add up to 1.0, or 100%, or another value.

[0166] Figure 17 is an example of a cross-section of a coronary artery 1600. In this example, the cross-section is a two-dimensional slice of the coronary artery 1600. In other examples, the cross-section can be a three-dimensional volume of the coronary artery 1600. Figure 17In some embodiments, the lumen wall 1606 of the artery 1600 with the inner portion 1602 and the outer vessel wall 1608 exhibits a gradient radio density in the lumen within the plaque 1604 between the lumen wall 1606 and the perivascular tissue 1620 outside the vessel. The line 1612 indicates a line through the diameter of the artery 1600.

[0167] Figure 17 is an image showing an example of a longitudinal straightened rendering of a coronary artery 1708, which shows plaque accumulation between the inside and outside of the coronary artery 1708. As shown, the coronary artery 1708 includes an inner lumen 1710 with a cavity 1702 within the inner lumen 1710 for transporting blood. The coronary artery 1708 also includes an outer vessel 1706 extending from the left side of the coronary artery 1708 (relative to the orientation of the image). Plaque 1704 is accumulated between the outer vessel 1706 and the inner lumen 1710. This image demonstrates the different compartments of the lumen, plaque, and perivascular tissue outside the inner lumen 1710 and the plaque 1702. Figure 18 Figure 18

[0168] Figure 18 is a graph illustrating a plot of compartment areas of cross sections of plaque 1801, lumen 1802, and fat 1803 along the length of a coronary artery. Such a plot 1801, 1802, 1803 can be generated for the left coronary artery and / or the right coronary artery. A plurality of cross-sectional areas of plaque of the coronary artery can be determined along the length of the coronary artery. The distance from the opening of each plaque, lumen, and fat cross section can also be determined. The ratio of the cross sections of plaque, lumen, and fat of one or more portions of one or more coronary arteries can be indicative of the patient’s risk associated with plaque. Figure 18 is an example of a plot of a plot of plaque cross sections 1801, lumen cross sections 1802, and fat cross sections 1803, where the distance from the opening of the cross sections 1801, 1802, 1803 of plaque, lumen, and fat is plotted on the x-axis, and the area of the respective cross sections 1801, 1802, 1803 of plaque, lumen, and fat is plotted on the y-axis. In some embodiments, the distance scale along the x-axis can be in millimeters, but another scale can be used in other embodiments. In some embodiments, the area of the cross sections can be in mm 2

[0169] In​​​Figure one In the illustrated example, along the entire distance away from the ostium, the cross-section of the lumen 1802 is generally smaller than the cross-section of the plaque 1801 and the cross-section of the fat 1803, and the cross-section of the plaque 1801 is smaller than the cross-section of the lumen 1802. At some distance away from the ostium (e.g., about distance 7 to 9 along the x-axis), one or more of the cross-sectional areas of the plaque, lumen, and / or fat are substantially similar or nearly identical, although even at these locations, the cross-section of the lumen 1802 is smaller than the cross-section of the plaque 1801, and the cross-section of the plaque 1801 is smaller than the cross-section of the fat 1803. However, at other distances away from the ostium, the cross-sectional differences are significant, and this difference is clear from the plaque cross-section plot 1801, the lumen cross-section plot 1802, and the fat cross-section plot 1803. For example, in the coronary artery cross-sections from about distance 16 (indicated by line “A”) to about distance 25 (indicated by line “B”) along the x-axis, the cross-sectional area of the fat 1803 is significantly larger than the cross-section of the plaque 1801, and the cross-sectional area of the plaque 1801 is significantly larger than the cross-sectional area of the lumen 1802. As a general example, at one particular distance “20” indicated by line “C”, the cross-sectional area of the lumen 1802 is about 9 square units, the cross-sectional area of the plaque 1801 is about 18 square units, and the cross-sectional area of the fat is about 24 square units. As a specific example, at the particular distance 20 mm indicated by the C line, the cross-sectional area of the lumen 1802 is about 9 mm 2 , the cross-sectional area of the plaque 1801 is about 18 mm 2 , and the cross-sectional fat area is about 24 mm 2 Thus, at distance “20”, the fat:plaque ratio = 1.33, the fat:lumen ratio = 2.67, and the plaque:fat ratio = 2.00. In some embodiments, one or more of the fat:plaque, fat:lumen, and / or plaque:fat ratios are computed for a plurality of distances, and this data can be provided on a display or in a report as numbers or in plots of the ratios. In one example, ratios that exceed a certain threshold can be flagged for further investigation of the portion of the coronary artery corresponding to where the ratio exceeds the certain threshold. In another example, ratios that exceed a certain threshold for a particular distance (a portion of the coronary artery) can be flagged for further investigation of the corresponding portion of the coronary artery. Such compartment ratios can be used to indicate a significant difference in risk associated with the plaque.

[0170] The ratios of plaque, lumen, and fat compartments along the coronary artery can also be based on total volume (e.g., based on cross-sectional areas of plaque, lumen, and fat over a distance), and the total volume can be included in the generated display or in a report. The ratios of plaque, lumen, and fat compartments along the coronary artery that are calculated based on total volume can be indicative of the patient’s risk associated with plaque. In one example, multiple portions of the coronary artery can be used to calculate multiple total volumes. In one example, for a point at which a cross-sectional area ratio (e.g., fat:plaque, fat:lumen, and / or plaque:fat) exceeds a certain threshold (or is above a certain threshold over a distance), this can mark the beginning point for calculating a total volume of the coronary artery. In one example, a point at which a cross-sectional area ratio (e.g., fat:plaque, fat:lumen, and / or plaque:fat) falls below a certain threshold (or falls below a certain threshold over a distance) can mark the end point for calculating a total volume of the coronary artery. Using the example of the graph in Figure 19 , at approximately line A, one or more of the cross-sectional area ratios (e.g., fat:plaque, fat:lumen, and / or plaque:fat) can exceed a certain threshold, and thus line A can mark the beginning point for calculating a volume of the coronary artery; and then at approximately line B, one or more of the cross-sectional area ratios (e.g., fat:plaque, fat:lumen, and / or plaque:fat) can fall below a certain threshold, and thus line B can mark the end point for calculating such a volume of the coronary artery. Although the portion of the graph corresponding to distances 16-25 generally shows the largest ratio, in some examples, smaller ratios can also indicate the beginning / end of total volume calculations. For example, starting at distance 34, the curves for plaque cross-section 1801, lumen cross-section 1802, and fat cross-section 1803 change (albeit not very much) to distance 46, and then again continue to change consistently (albeit not very much) to distance 56. In some examples, when one or more of the cross-sectional area ratios (e.g., fat:plaque, fat:lumen, and / or plaque:fat) are such that they exceed a certain threshold for a certain distance, it can be determined that a total volume will be calculated for a portion of the coronary artery, and the results are marked on the display or in a generated report for further study of the corresponding portion of the coronary artery. Such compartment ratios can be used to indicate significant differences in risk associated with plaque. Figure 18

[0171] ​​​In some implementations, other compartment ratios (e.g., fat: plaque, fat: lumen, and / or plaque: fat) can be calculated and provided on a display or in a report to provide additional information about the patient's coronary arteries. Such information can indicate portions of the arteries for further study or can indicate the patient's condition. One or more compartment ratios can be determined at a first point in time (e.g., at a starting date) and then again at a subsequent (second) point in time (e.g., 2 months later, 6 months later, 1 year later, 2 years later, etc.) to track any changes that occur in the patient over a subject period of time. The period of time can be, for example, from 1 week or two weeks to several months or years. In one example, one or more compartment ratios can be determined at a first point in time (e.g., when the patient is 50 years old) and then again at a second point in time (e.g., when the patient is 60 years old) to track any changes that occur in the patient over a certain period of time. In one example, for a patient with a family history of coronary artery problems, such testing can be performed when the patient is 40 years old and then every 5 or 10 years thereafter to gather information about the patient's coronary arteries that can indicate changes in the coronary arteries that can indicate the onset of a coronary artery problem / disease.

[0172] In some implementations, compartment ratios for segments of the coronary arteries can be determined. For example, the compartment ratios for one or more of the following: proximal segment of the right coronary artery, middle segment of the right coronary artery, distal segment of the right coronary artery, posterior intracerebral branch of the right coronary artery, left main coronary artery, intracerebral internal branch of the left coronary artery, intracerebral internal branch of the middle segment of the left coronary artery, intracerebral internal branch of the distal segment of the left coronary artery, first diagonal branch, second diagonal branch, circumflex branch of the proximal segment of the left coronary artery, first marginal branch, circumflex branch of the middle segment of the left coronary artery, second marginal branch, circumflex branch of the distal segment of the left coronary artery, posterior intraventricular branch of the left coronary artery, right coronary artery, and / or intermediate atrial branch of the right coronary artery. In some implementations, compartment ratios can be determined for any coronary vessel. Information related to the determined compartment ratios for a patient can be presented on one or more paper or electronic reports, graphs, charts, etc. In some implementations, two or more compartment ratios for a particular patient are determined and presented as a patient aggregate of compartment ratios. For example, compartment ratios for two or more portions of the right coronary artery of a patient can be presented and reported on a display to indicate the compartment ratios for those portions of the right coronary artery. In some examples, compartment ratios for all portions of the right coronary artery are determined and presented for evaluation. In some examples, compartment ratios for all portions of the left coronary artery are determined and presented for evaluation. In some examples, compartment ratios for corresponding portions of the left and right coronary arteries are determined and presented for evaluation. Various embodiments of compartment ratios can be included with any other information described herein to indicate plaque risk.

[0173] Figure 19is another plot chart illustrating compartment areas of cross-sections of plaque 1901, lumen 1902, and fat 1903 along the length of a coronary artery. As with Figure 18 Similarly, the distance of the measured cross-sections of plaque, lumen, and fat from the opening of the coronary artery is plotted on the x-axis, and the area of the respective cross-section of plaque is plotted on the y-axis. Examples of certain embodiments The coronary artery in FIG. 1 1 illustrates an example of different compartment ratios than the artery in FIG. 1 1. Implementation system and terminology The coronary artery in FIG. 1 1 illustrates an example of different compartment ratios than the artery in FIG. 1 1.

[0174] Figure 1

[0175] The following are non-limiting examples of certain embodiments of systems and methods of characterizing coronary plaque. Other embodiments can include one or more other or different features discussed herein.

[0176] Embodiment 1 : A method of characterizing coronary plaque tissue data and perivascular tissue data using image data collected from computed tomography (CT) scans along a blood vessel, the image information including radiodensity values and locations of coronary plaque and radiodensity values of perivascular tissue adjacent to the coronary plaque, the method comprising: quantifying radiodensity in a region of the coronary plaque in the image data; quantifying radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data; determining a gradient of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue; determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing one or more of: the gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular tissue, or the ratio of the radiodensity values of the coronary plaque and the corresponding perivascular tissue, wherein the method is performed by one or more computer hardware processors configured to execute computer executable instructions on a non-transitory computer storage medium.

[0177] Embodiment 2: The method of embodiment 1, wherein the perivascular tissue includes at least one of a coronary artery lumen, fat, coronary plaque, or myocardium.

[0178] Embodiment 3: The method of any one of embodiments 1 and 2, further comprising receiving the image data at a data storage component via a network.

[0179] Embodiment 4: The method of embodiment 3, wherein the network is one of the Internet or a wide area network (WAN).

[0180] Embodiment 5: The method of any one of embodiments 1 to 4, wherein the image data from the CT scans includes at least ten images.

[0181] Example 6: The method of any of Examples 1-4, wherein the image data from the CT scan comprises at least 30 images.

[0182] Example 7: The method of any of Examples 1-6, further comprising generating a patient report based on the characterization of the coronary plaque, the patient report comprising at least one of a diagnosis, a prognosis, or a recommended treatment for the patient.

[0183] Example 8: The method of any of Examples 1-7, wherein quantifying the radiodensity in the at least one region of the perivascular tissue comprises: quantifying the radiodensity of the scan information for the coronary plaque and the adipose tissue in each of the one or more regions or layers of the perivascular tissue.

[0184] Example 9: The method of any of Examples 1-8, wherein the radiodensity of the scan information is quantified for water in each of the one or more regions of the coronary plaque and the region of the perivascular tissue.

[0185] Example 10: The method of any of Examples 1-9, wherein the radiodensity of the scan information is quantified for low radiodensity plaque in each of the one or more regions or layers of the coronary plaque.

[0186] Example 11: The method of any of Examples 1-10, wherein the coronary plaque radiodensity value and the perivascular tissue radiodensity value are average radiodensities.

[0187] Example 12: The method of any of Examples 1-10, wherein the coronary plaque radiodensity value and the perivascular tissue radiodensity value are maximum radiodensities.

[0188] Example 13: The method of any of Examples 1-10, wherein the coronary plaque radiodensity value and the perivascular tissue radiodensity value are minimum radiodensities.

[0189] Example 14: The method of any of Examples 1-13, wherein the quantified radiodensities are transformed numerical radiodensity values of the image data.

[0190] Example 15: The method of any of Examples 1-14, wherein the quantified radiodensities account for patient and CT-specific parameters comprising one or more of: iodine-containing contrast agent, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal-to-noise ratio, contrast-to-noise ratio, tube voltage, milliamp, cardiac gating method, single-energy and multi-energy image acquisition, CT scanner type, heart rate, heart rhythm, or blood pressure.

[0191] Example 16: The method of any of Examples 1-15, further comprising reporting the quantified radiodensity of the coronary artery plaque and the perivascular tissue as a gradient.

[0192] Example 17: The method of any of Examples 1-16, wherein the quantified radiodensity of the coronary artery plaque and the perivascular tissue is reported as a ratio of a slope of the gradient from the coronary artery plaque to the perivascular tissue adjacent to the coronary artery plaque.

[0193] Example 18: The method of any of Examples 1-17, wherein the quantified radiodensity of the coronary artery plaque and the perivascular tissue is reported as a difference in radiodensity values from the coronary artery plaque to the perivascular tissue.

[0194] Example 19: The method of any of Examples 1-18, wherein the image data is collected from a CT scan along a length of at least one of the right coronary artery, the left anterior descending artery, the left circumflex artery, or a branch of any of the foregoing, or the aorta, or the carotid artery, or the femoral artery, or the renal artery.

[0195] Example 20: The method of any of Examples 1-18, wherein the data is collected from a CT scan along a length of a non-coronary reference vessel.

[0196] Example 21: The method of Example 20, wherein the non-coronary reference vessel is the aorta.

[0197] Example 22: The method of any of Examples 1-21, wherein the radiodensity is quantified in Hounsfield units.

[0198] Example 23: The method of any of Examples 1-21, wherein the radiodensity is quantified in absolute material density when performed with multi-energy CT.

[0199] Example 24: The method of any of Examples 1-24, wherein the one or more regions or layers of perivascular tissue extend to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity of adipose tissue (i) reaches a minimum value within the scanned anatomical site in a healthy blood vessel, or (ii) drops by a relative percentage (e.g., >= 10%), or (iii) drops by a relative percentage relative to a baseline radiodensity value in a blood vessel of the same type without disease.

[0200] Example 25: The method of any one of Examples 1 to 25, wherein one or more regions or layers of the coronary plaque extend to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity of adipose tissue (i) reaches a maximum within the plaque, or (ii) increases by a relative percentage (e.g., >= 10%), or (iii) changes by a relative percentage relative to the lowest radiodensity value in the plaque.

[0201] Example 26: The method of Example 24, wherein the baseline radiodensity value is an average radiodensity quantified in a layer of perivascular tissue located within a fixed layer or region around the outer blood vessel wall, and is measured by thickness, area, or volume.

[0202] Example 27: The method of Example 24, wherein the baseline perivascular tissue radiodensity is a radiodensity quantified for adipose tissue in a layer of perivascular tissue located near the outer wall of the blood vessel.

[0203] Example 28: The method of Example 24, wherein the baseline perivascular tissue radiodensity is a radiodensity quantified for water in a layer of perivascular tissue located near the outer wall of the blood vessel.

[0204] Example 29: The method of Example 24, wherein the baseline radiodensity is an average radiodensity.

[0205] Example 30: The method of Example 24, wherein the baseline radiodensity is a maximum radiodensity.

[0206] Example 31: The method of Example 24, wherein the baseline radiodensity is a minimum radiodensity.

[0207] Example 32: The method of Example 24, wherein the baseline radiodensity value is an average radiodensity quantified in a layer of coronary plaque tissue within a fixed layer or region within the plaque, and is measured by thickness, area, or volume.

[0208] Example 33: The method of Example 24, wherein the baseline coronary plaque radiodensity is a radiodensity quantified for all coronary plaque in the measured blood vessel.

[0209] Example 34: The method of any of Examples 1-33, further comprising: determining a plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, the farthest to the endmost distance; determining an area of a region bounded by the plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, the farthest to the endmost distance, and a plot of the baseline radiodensity as a function of distance to the outer wall of the vessel, the farthest to the endmost distance; and dividing the area by the quantified radiodensity measured at a distance from the outer wall of the vessel, wherein the distance is less than the radius of the vessel, or is a distance from the outer surface of the vessel above which the quantified radiodensity of the adipose tissue decreases more than 5% compared to the baseline radiodensity of the adipose tissue in a non-diseased vessel of the same type.

[0210] Example 35: The method of any of Examples 1-34, further comprising: determining a plot of the quantified change in radiodensity as a function of distance from the outer wall of the vessel, the farthest to the inner surface of the plaque; determining an area of a region bounded by the plot of the quantified change in radiodensity as a function of distance to the outer wall of the vessel, the farthest to the inner surface of the plaque; and dividing the area by the quantified radiodensity measured at a distance to the outer wall of the vessel, wherein the distance is less than the radius of the vessel, or is a distance from the outer surface of the vessel above which the quantified radiodensity of the adipose tissue decreases more than 5% compared to the baseline radiodensity of the adipose tissue in a non-diseased vessel of the same type.

[0211] Example 36: The method of Example 25, wherein the quantified radiodensity is a quantified radiodensity of adipose tissue in each of one or more regions or layers of the perivascular tissue or coronary plaque.

[0212] Example 37: The method of Example 25, wherein the quantified radiodensity is a quantified radiodensity of water in each of one or more regions or layers of the perivascular tissue.

[0213] Example 38: The method of Example 25, wherein the quantified radiodensity is an average radiodensity.

[0214] Example 39: The method of Example 25, wherein the quantified radiodensity is a maximum radiodensity.

[0215] Example 40: The method of Example 25, wherein the perivascular tissue extends to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity of the adipose tissue (i) reaches a minimum within the scanned anatomical structure in healthy blood vessels; or (ii) decreases by a relative percentage (e.g., >= 10%); or (iii) decreases by a relative percentage from a baseline radiodensity value in the same type of blood vessel without disease.

[0216] Example 41 : The method of any one of Examples 1-40, wherein one or more regions or layers of the coronary artery plaque tissue extend to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity of the adipose tissue (i) reaches a maximum within the plaque; or (ii) increases by a relative percentage (e.g., >= 10%); (iii) or changes by a relative percentage from a lowest radiodensity value in the plaque.

[0217] Example 42: The method of any one of Examples 1-41, further comprising: normalizing the quantified radiodensities of the coronary artery plaque and perivascular tissue for CT scan parameters (patient and CT specific parameters), the CT scan parameters including one or more of: iodine-containing contrast agent, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal-to-noise ratio, contrast-to-noise ratio, tube voltage, milliamp, cardiac gating method, single energy and multi-energy image acquisition, CT scanner type, heart rate, heart rhythm, or blood pressure; normalizing the quantified radiodensities of the perivascular fat associated with the coronary artery plaque for remote perivascular fat; and normalizing the quantified radiodensities of the coronary artery plaque for remote coronary artery plaque.

[0218] Example 43: The method of any one of Examples 1-42, further comprising: quantifying other high-risk plaque features, such as remodeling, bulk, and punctate calcification, and further characterizing the high-risk plaque based on one or more of the high-risk plaque features.

[0219] Example 44: The method of any one of the preceding Examples, wherein characterizing the coronary artery plaque is based on plaque heterogeneity, including a mixture of calcified and non-calcified plaque.

[0220] Example 45: The method of any one of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high-risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely to be implicated as a culprit lesion for a future acute coronary event.

[0221] Example 46: The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely to cause ischemia.

[0222] Example 47: The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely to cause vasospasm.

[0223] Example 48: The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely to progress rapidly.

[0224] Example 49: The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely not to calcify.

[0225] Example 50: The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is likely not to calcify.

[0226] Example 51 : The method of any of the preceding Examples, wherein characterizing the coronary artery plaque comprises: identifying the coronary artery plaque as a high risk plaque based on a comparison to previously classified patient image data if the coronary artery plaque is associated with complications at the time of revascularization, such as by inducing a no-reflow phenomenon.

[0227] Example 52: A system for volumetrically characterizing coronary plaque tissue data and perivascular tissue data using image data collected from one or more computed tomography (CT) scans along a blood vessel, the image information including radiodensity values of a coronary plaque and perivascular tissue adjacent to the coronary plaque, the system comprising: a first non-transitory computer storage medium configured to store at least the image data; a second non-transitory computer storage medium configured to store at least computer-executable instructions; and one or more computer hardware processors in communication with the second non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to at least: quantify radiodensities in a region of the coronary plaque in the image data; quantify radiodensities of at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data; determine a gradient of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; determine a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterize the coronary plaque by analyzing one or more of: the gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular tissue, or the ratio of the coronary plaque radiodensity values and the corresponding perivascular tissue radiodensity values.

[0228] Example 53: A non-transitory computer-readable medium comprising instructions that, when executed, cause an apparatus to perform a method comprising: quantifying radiodensities in a region of a coronary plaque in image data; quantifying radiodensities of at least one region of corresponding perivascular tissue adjacent to the coronary plaque in the image data; determining a gradient of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing one or more of: the gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular tissue, or the ratio of the coronary plaque radiodensity values and the corresponding perivascular tissue radiodensity values.

[0229] Example 54: A system comprising a processor and a non-transitory storage medium comprising processor-executable instructions to implement a processing system for characterizing coronary artery plaque, the processing system configured to: quantify, in image data, radiodensity in a region of coronary artery plaque; quantify, in the image data, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary artery plaque; characterize one or more medical conditions based on the quantified radiodensity properties of the coronary artery plaque and the radiodensity in the at least one region of corresponding perivascular tissue adjacent to the coronary artery plaque using at least one of: a ratio of quantified radiodensity values within the coronary artery plaque and the corresponding perivascular tissue, or at least one gradient of quantified radiodensity values in the coronary artery plaque and the corresponding perivascular tissue.

[0230] Example 55: A non-transitory computer-readable medium comprising instructions that, when executed, cause an apparatus to perform a method comprising: quantifying, in image data, radiodensity in a region of coronary artery plaque; quantifying, in the image data, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary artery plaque; characterizing one or more medical conditions based on the quantified radiodensity properties of the coronary artery plaque and the radiodensity in the at least one region of corresponding perivascular tissue adjacent to the coronary artery plaque using at least one of: a ratio of quantified radiodensity values within the coronary artery plaque and the corresponding perivascular tissue, or at least one gradient of quantified radiodensity values in the coronary artery plaque and the corresponding perivascular tissue.

[0231] Figure 1

[0232] Implementations disclosed herein provide systems, methods, and apparatuses for maskless phase detection autofocus. Those skilled in the art will recognize that these embodiments can be implemented in hardware, software, firmware, or any combination thereof.

[0233] In some embodiments, the circuits, processes, and systems discussed above can be utilized in a wireless communication device, such as a mobile wireless device. A wireless communication device can be an electronic device used to communicate wirelessly with other electronic devices. Examples of wireless communication devices include cellular telephones, smartphones, personal digital assistants (PDAs), e-readers, gaming systems, music players, netbooks, wireless modems, laptop computers, tablet devices, and the like, such as the mobile wireless device 130C Figure 1 ) of FIG. 1.

[0234] A wireless communication device can include one or more image sensors, one or more image signal processors, and a memory including instructions or modules for performing the processes described above. The device can also have data, a processor to load instructions and / or data from memory, one or more communication interfaces, one or more input devices, one or more output devices such as a display device, and a power source / interface. The wireless communication device can additionally include a transmitter and a receiver. The transmitter and receiver can be collectively referred to as a transceiver. The transceiver can be coupled to one or more antennas for transmission and / or reception of wireless signals.

[0235] The wireless communication device can be wirelessly connected to another electronic device (e.g., a base station) to communicate information. For example, to communicate information received from the processing system 120 ​ ) to / from another device 130 ​ ). The wireless communication device can alternatively be referred to as a mobile device, mobile station, subscriber station, user device (UE), remote station, access terminal, mobile terminal, terminal, user terminal, subscriber unit, etc. Examples of wireless communication devices include a laptop or desktop computer, cell phone, smart phone, tablet device, etc. The wireless communication device can operate in accordance with one or more industry standards. Thus, the general term “wireless communication device” or “mobile device” can include wireless communication devices described by different nomenclatures according to industry standards (e.g., access terminal, user equipment (UE), remote terminal, etc.).

[0236] The functions described herein can be stored as one or more instructions on a processor-readable or computer-readable medium. For example, on any of the processing system 120 or devices 130. The term “computer-readable medium” refers to any available medium that can be accessed by a computer or processor. By way of example, and not limitation, such a medium can comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and optical disks, where disks usually reproduce data magnetically, while optical disks reproduce data optically with lasers. It should be noted that the computer-readable medium can be tangible and non-transitory. The term “computer program product” refers to the computer equipment or processor in combination with code or instructions (e.g., a “program”) that can be executed, processed or computed by the computer equipment or processor. As used herein, the term “code” can refer to software, instructions, code or data that is executable by the computer equipment or processor.

[0237] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions can be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order is required for proper operation of the method that is described, the order or use of specific steps and / or actions can be modified without departing from the scope of the claims. Thus, the following description is meant to be exemplary only and is not intended to be limiting.

[0238] It should be noted that the terms“couple,”“coupling,”“coupled” or other variations of the word couple as used herein can indicate either an indirect connection or direct connection. For example, if a first component is“coupled” to a second component, then the first component can be either indirectly connected to the second component or directly connected to the second component. As used herein, the term“plurality” denotes two or more. For example, a plurality of components indicates two or more components.

[0239] The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving, investigating, looking up (such as looking up in a table, a database or another data structure), ascertaining and the like. Also,“determining” can include receiving (such as receiving information), accessing (such as accessing data in a memory) and the like. Also, “determining” can include resolving, selecting, choosing, establishing and the like.

[0240] The phrase“based on” is not intended to mean“based only on,” unless expressly specified otherwise. In other words, the phrase“based on” describes both“based only on” and“based at least on.”

[0241] In the preceding description, specific details are given to provide a thorough understanding of the examples. However, a person having ordinary skill in the art will understand that the examples can be practiced without these specific details. For example, electrical components / devices can be shown in block diagram form, rather than in detail, in order to avoid obscuring the examples. In other instances, such components, other structures and techniques can be shown in detail to further convey the principles and new features of the examples. Accordingly, the application is not intended to be limited by the examples shown herein but is to be accorded the widest scope consistent with the principles and new features disclosed herein.

Claims

1. A computer-implemented method of assessing risk of coronary artery plaque by analyzing radiodensity values of coronary artery plaque using image data collected from a computed tomography (CT) scan along a blood vessel, the method comprising: accessing image data of a CT scan along the blood vessel, the image data including radiodensity values of coronary artery plaque; quantifying the radiodensity values of the coronary artery plaque, wherein quantifying the radiodensity values of the coronary artery plaque comprises: identifying a region of interest of the blood vessel in the image data; identifying coronary artery plaque in the blood vessel based at least in part on the identified region of interest of the blood vessel; and determining radiodensity values of pixels within the identified coronary artery plaque; characterizing different types of coronary artery plaque based at least in part on the quantified radiodensity values of coronary artery plaque; and determining whether the blood vessel is likely ischemic based at least in part on applying a machine learning algorithm, wherein inputs to the machine learning algorithm include one or more of the identified region of interest of the blood vessel, the quantified radiodensity values of coronary artery plaque, and the characterized different types of coronary artery plaque, wherein the characterization of different types of coronary artery plaque and the determination of whether the blood vessel is likely ischemic are configured to be used for assessing risk of coronary artery plaque.

2. The method of claim 1, further comprising determining radiodensity values of perivascular tissue, wherein inputs to the machine learning algorithm further include the radiodensity values of perivascular tissue, wherein the perivascular tissue includes at least one of a coronary artery lumen, fat, coronary artery plaque, or myocardium.

3. The method of claim 1 or 2, wherein the image data is accessed from a data storage component via a network.

4. The method of claim 3, wherein the network is one of the Internet or a wide area network (WAN).

5. The method of claim 1, wherein the image data from the CT scan includes at least ten images.

6. The method of claim 1, wherein the image data from the CT scan includes at least 30 images.

7. The method of claim 1, further comprising generating a patient report based on the characterization of the different types of coronary artery plaque, the patient report including at least one of a diagnosis, prognosis, or recommended treatment for a patient.

8. The method of claim 2, wherein determining a radiodensity value of the perivascular tissue comprises: quantifying radiodensity of the image data for coronary artery plaque and adipose tissue in each of one or more regions or layers of perivascular tissue.

9. The method of claim 1, wherein the radiodensity values are transformed numerical radiodensity values of the image data.

10. The method of claim 1, wherein the radiodensity values account for patient and CT-specific parameters including one or more of: iodine-containing contrast, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal-to-noise ratio, contrast-to-noise ratio, tube voltage, milliamp, cardiac gating method, single-energy and multi-energy image acquisition, CT scanner type, heart rate, heart rhythm, or blood pressure.

11. The method of claim 1, further comprising reporting the quantified radiodensity values of the coronary artery plaque as a gradient.

12. The method of claim 11, wherein the quantified radiodensities of the coronary artery plaque and the perivascular tissue are reported as a ratio of a slope of the gradient from the coronary artery plaque to the perivascular tissue adjacent to the coronary artery plaque.

13. The method of claim 2, wherein the quantified radiodensities of the coronary artery plaque and the perivascular tissue are reported as a difference in radiodensity values from the coronary artery plaque to the perivascular tissue.

14. The method of claim 1, wherein the image data is collected from a CT scan along a length of at least one of a right coronary artery, a left anterior descending artery, a left circumflex artery, or a branch of the foregoing, or a main aorta, or a carotid artery, or a femoral artery, or a renal artery.

15. The method of claim 1, wherein the image data comprises a CT scan along a length of a non-coronary reference vessel.

16. The method of claim 15, wherein the non-coronary reference vessel is a main aorta.

17. The method of claim 1, wherein the radiodensity values are quantified in Hounsfield units.

18. The method of claim 1, wherein the radiodensity values are quantified in absolute material density when a multi-energy CT is performed.

19. The method of claim 2, wherein the perivascular tissue extends to an end distance from an outer wall of the vessel, the end distance being a fixed distance at which the radiodensity value of fat tissue (i) reaches a minimum value within the scanned anatomical site in a healthy vessel, or (ii) decreases by a relative percentage of at least 10%; or (iii) decreases by a relative percentage from a baseline radiodensity value in a same type of vessel without disease.

20. The method of claim 1, wherein the coronary artery plaque extends to an end distance from an outer wall of the vessel, the end distance being a fixed distance at which the radiodensity value of fat tissue (i) reaches a maximum value within the coronary artery plaque, or (ii) increases by a relative percentage of at least 10%; or (iii) changes by a relative percentage from a lowest radiodensity value in the coronary artery plaque.

21. The method of claim 19, wherein the baseline radiodensity value is an average radiodensity quantified in a layer of perivascular tissue located within a fixed layer or region around the outer wall of the vessel, and is measured by thickness, area, or volume.

22. The method of claim 19, wherein the baseline radiodensity value is a radiodensity quantified for fat tissue in a layer of perivascular tissue located near the outer wall of the vessel.

23. The method of claim 19, wherein the baseline radiodensity value is a radiodensity quantified for water in a layer of perivascular tissue located near the outer wall of the vessel.

24. The method of claim 19, wherein the baseline radiodensity is an average radiodensity.

25. The method of claim 19, wherein the baseline radiodensity is a maximum radiodensity.

26. The method of claim 19, wherein the baseline radiodensity is a minimum radiodensity.

27. The method of claim 19, wherein the baseline radiodensity value is an average radiodensity quantified in a layer of coronary artery plaque tissue within a fixed layer or region within the plaque, and is measured by thickness, area, or volume.

28. The method of claim 19, wherein the baseline radiodensity value is a baseline coronary artery plaque radiodensity, wherein the baseline coronary artery plaque radiodensity is a radiodensity quantified for all coronary artery plaque in the measured vessel.

29. The method of claim 1, further comprising: determining a plot of a change in a quantified radiodensity value as a function of distance to an outer wall of the vessel, most distal to a terminal distance, the change in the quantified radiodensity value being relative to a baseline radiodensity value in each of one or more concentric layers of perivascular tissue; determining an area of a region bounded by the plot of the change in the quantified radiodensity value as a function of distance to the outer wall of the vessel, most distal to the terminal distance, and a plot of baseline radiodensity value as a function of distance from the outer wall of the vessel, most distal to the terminal distance; and dividing the area by a quantified radiodensity value measured at a distance from the outer wall of the vessel, wherein the distance is less than a radius of the vessel, or is a distance from the outer wall of the vessel above which a quantified radiodensity value of adipose tissue decreases more than 5% compared to the baseline radiodensity value of adipose tissue in a same type of vessel without disease.

30. The method of claim 1, further comprising: determining a plot of a change in a quantified radiodensity value as a function of distance to an outer wall of the vessel, most distal to an inner surface of the coronary artery plaque, the change in the quantified radiodensity value being relative to a baseline radiodensity value in each of one or more concentric layers of coronary artery plaque tissue; determining an area of a region bounded by the plot of the change in the quantified radiodensity value as a function of distance to the outer wall of the vessel, most distal to the inner surface of the coronary artery plaque, and a plot of baseline radiodensity value as a function of distance from the outer wall of the vessel, most distal to the inner surface of the coronary artery plaque; and dividing the area by a quantified radiodensity value measured at a distance from the outer wall of the vessel, wherein the distance is less than a radius of the vessel, or is a distance from the outer wall of the vessel above which a quantified radiodensity value of adipose tissue decreases more than 5% compared to the baseline radiodensity value of adipose tissue in a same type of vessel without disease.

31. The method of claim 30, wherein the quantified radiodensity values are quantified radiodensity values of adipose tissue in each of one or more regions or layers of the perivascular tissue or the coronary artery plaque.

32. The method of claim 30, wherein the quantified radiodensity values are quantified radiodensities of water in each of one or more regions or layers of the perivascular tissue.

33. The method of claim 30, wherein the quantified radiodensity values are average radiodensities.

34. The method of claim 30, wherein the quantified radiodensity values are maximum radiodensities.

35. The method of claim 29, wherein the perivascular tissue extends to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity values of adipose tissue (i) reach a minimum value within the scanned anatomical structure in a healthy blood vessel; or (ii) drop by a relative percentage; or (iii) drop by a relative percentage from a baseline radiodensity value in a same type of blood vessel without disease.

36. The method of claim 35, wherein one or more regions or layers of coronary artery plaque tissue extend to an end distance from the outer wall of the blood vessel, the end distance being a fixed distance at which the radiodensity values of adipose tissue (i) reach a maximum value within the plaque; or (ii) increase by a relative percentage; (iii) or change by a relative percentage from the lowest radiodensity value in the coronary artery plaque.

37. The method of claim 1, further comprising: normalizing the quantified radiodensity values of the coronary artery plaque for CT scan parameters, the CT scan parameters including one or more of: iodine-containing contrast agent, contrast type, injection rate, aortic contrast opacification, left ventricular blood pool opacification, signal-to-noise ratio, contrast-to-noise ratio, tube voltage, milliamp, cardiac gating method, single-energy and multi-energy image acquisition, CT scanner type, heart rate, heart rhythm, or blood pressure; normalizing the quantified radiodensity values of perivascular tissue associated with the coronary artery plaque for remote perivascular tissue; and normalizing the quantified radiodensities of the coronary artery plaque for remote coronary artery plaque.

38. The method of claim 1, further comprising quantifying other high-risk plaque features, and further characterizing the high-risk plaque based on one or more of the other high-risk plaque features.

39. The method of claim 38, wherein the other high-risk plaque features include remodeling, bulk or spotty calcification.

40. The method of claim 1, wherein characterizing the different types of coronary artery plaque is further based in part on plaque heterogeneity, including a mixture of calcified and non-calcified plaque.

41. The method of claim 1, wherein characterizing the different types of coronary artery plaques further comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to be involved as a culprit lesion for a future acute coronary event.

42. The method of claim 1, wherein characterizing the different types of coronary artery plaques further comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to cause ischemia.

43. The method of claim 1, wherein characterizing the different types of coronary artery plaques further comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to cause vasospasm.

44. The method of claim 1, wherein characterizing the different types of coronary artery plaques further comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to progress rapidly.

45. The method of claim 1, wherein characterizing the different types of coronary artery plaques further comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to not calcify.

46. The method of claim 1, wherein characterizing the different types of coronary artery plaques comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is likely to not respond to medical therapy, regress, or stabilize.

47. The method of claim 1, wherein characterizing the different types of coronary artery plaques comprises: based on a comparison with previously classified patient image data, the coronary plaque is identified as a high risk plaque if the coronary plaque is associated with complications at the time of revascularization.

48. A system for assessing risk of a coronary plaque using image data collected from one or more computed tomography (CT) scans along a blood vessel by analyzing radiodensity values of the coronary plaque, the system comprising: a first non-transitory computer storage medium configured to store at least the image data; a second non-transitory computer storage medium configured to store at least computer-executable instructions; and one or more computer hardware processors in communication with the second non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer-executable instructions to at least: access image data of the CT scans along the blood vessel, the image data including radiodensity values of a coronary plaque; quantify radiodensity values of a coronary plaque in the image data, wherein quantifying the radiodensity values of the coronary plaque comprises: identifying a region of interest of the blood vessel in the image data; identifying a coronary plaque in the blood vessel based at least in part on the identified region of interest of the blood vessel; and determining radiodensity values of pixels within the identified coronary plaque; characterize different types of coronary plaque based at least in part on the quantified radiodensity values of the coronary plaque; and determine whether the blood vessel is likely ischemic based at least in part on applying a machine learning algorithm, wherein inputs to the machine learning algorithm include one or more of the identified region of interest of the blood vessel, the quantified radiodensity values of the coronary plaque, and the characterized different types of coronary plaque, wherein the characterization of different types of coronary plaque and the determination of whether the blood vessel is likely ischemic are configured to be used to assess risk of the coronary plaque.

49. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause an apparatus to perform a method comprising: accessing image data of a CT scan along a blood vessel, the image data comprising radiodensity values of coronary plaque; quantifying radiodensity values of coronary plaque, wherein quantifying the radiodensity values of coronary plaque comprises: identifying a region of interest of the blood vessel in the image data; identifying coronary plaque in the blood vessel based at least in part on the identified region of interest of the blood vessel; and determining radiodensity values of pixels within the identified coronary plaque; characterizing different types of coronary plaque based at least in part on the quantified radiodensity values of coronary plaque; and determining whether the blood vessel is likely ischemic based at least in part on applying a machine learning algorithm, wherein input to the machine learning algorithm comprises one or more of the identified region of interest of the blood vessel, the quantified radiodensity values of coronary plaque, and the characterized different types of coronary plaque, wherein the characterization of different types of coronary plaque and the determination of whether the blood vessel is likely ischemic are configured to be used for assessing risk of coronary plaque.

Images