32 results about "Coronary plaque" patented technology

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

The invention relates to a coronary artery plaque rupture risk assessment method based on a non-local physical information neural network, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, introducing a near-field dynamics theory, reconstructing a mechanical constitutive equation of plaque rupture, and deducing a crack propagation criterion; s2, constructing a non-local physical information neural network, taking geometric structure information and material attributes of the coronary artery plaque as input, and taking a displacement field and a stress field of the plaque as output; taking the plaque mechanics basic equation as a physical constraint condition, and adding a loss function; s3, constructing a data set; s4, training the physical information neural network by using the training set, and adjusting network parameters by using a stochastic gradient descent optimization algorithm to minimize a loss function; optimizing and testing the model; and S5, inputting a coronary artery medical image number to be evaluated into the trained non-local physical information neural network, and outputting a rupture risk evaluation result of the coronary artery plaque.

The invention belongs to the technical field of medical image data processing, and particularly relates to a cardiac coronary plaque simulation method and system based on a digital phantom, and the method comprises the step of generating reconstructed simulation CT projection images with different halo artifact degrees. The method solves the problems that the effectiveness of an artifact removal method cannot be verified and evaluated due to the fact that the algorithm training effect is not generalized enough, and has the beneficial technical effects that the effectiveness of the artifact removal method is fully verified and evaluated, and the generalization of a deep learning halo artifact removal algorithm is improved.

The invention discloses a coronary plaque biomechanical property calculation method, and belongs to the field of computational biofluid mechanics in biomedical engineering. According to the method, the influence of cardiac pulsation, coronary blood vessel complex geometric distribution characteristics and coronary plaque composition factors is integrally considered, and the coronary plaque biomechanical characteristics are calculated; wherein the number of the fluid computational domains is two, and the two fluid computational domains are respectively a coronary vessel intracavity blood flow area and a plaque tissue fluid flow area surrounded by a fibrous membrane and a blood vessel; the two structural computational domains are respectively an elastic blood vessel wall and an ultrathin flexible plaque fiber membrane; the plaque fiber membrane generates deformation movement under the pressure of blood in blood vessels on two sides and tissue fluid in the plaque; the motion of the coronary plaque fibrous membrane is described by adopting an independently researched and developed immersion film method, and compared with a traditional coronary plaque biomechanical characteristic calculation method, the method has better information integrity, better accuracy and wider applicability.

Systems and methods of predicting microcalcification activity in a vascular vessel comprising either an artery or a vein, comprising the steps of: (a) measuring patient data comprising one or more of: the existence of and / or quantity of coronary plaques or visible markers of disease in a vascular tissue sample; the existence of and / or quantity of healthy tissue in the vascular tissue sample; one or more features that define an abnormal hemodynamic environment in a vessel; one or more geometric features that are associated with vascular remodeling and which influence hemodynamics in a vessel, and / or one or more material properties that influence vascular hemodynamics; and (b) calculating the microcalcification activity in the vessel as a function of the measurements taken in Step (a).

The application provides a method and device for comprehensively evaluating coronary plaque risk based on medical images. The method for comprehensively evaluating coronary plaque risk based on medical images comprises the following steps: acquiring a coronary strain parameter according to a coronary tree segmented image; acquiring plaque morphology parameter information according to a plaque segmented image; acquiring hemodynamic parameter information according to a resting state time sequence image; and acquiring a plaque risk index according to the coronary strain parameter, the plaque morphology parameter information and the hemodynamic parameter information. The application firstly segments a heart structure and calculates a coronary strain parameter according to the morphology of the coronary artery and the strain difference between different time phases. The plaque morphology parameter is calculated according to the position, morphology and texture structure of the plaque. Then, the computational fluid dynamics method is used to calculate the hemodynamic parameter of the coronary artery. Finally, a comprehensive index is designed to jointly determine the coronary plaque risk.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

This invention provides a coronary CTA calcified plaque detection system, method, device, and medium based on a phantom, relating to the field of applied medical technology. In this embodiment, after adjusting the CT equipment, at least one CTA image of all plaque phantoms can be acquired. After acquiring the patient's coronary CTA, the area adjacent to the plaque location is analyzed by computer. The analysis results are then matched with the phantom image analysis results using the same algorithm and designed indicators to select the phantom that best simulates the plaque situation in the current CTA. This phantom information is used to assist in reconstructing the true condition of the plaque on the CTA. Therefore, this embodiment of the invention can use plaque phantoms to match CTA to assist in reconstructing the true size of coronary plaques on CTA, increasing the reliability of non-invasive coronary CTA in plaque analysis.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

A computer-implemented method includes obtaining an image of one or more anatomical vessels of a subject, wherein the image is a segmentation of the one or more anatomical vessels from image data acquired with a three-dimensional medical imaging modality. The computer-implemented method further includes obtaining one or more segmentations of plaque in the one or more anatomical vessels of the subject. The computer-implemented method further includes obtaining Fractional Flow Reserve (FFR) information for the one or more anatomical vessels of the subject. The computer-implemented method further includes generating a composite rendering that includes the image of one or more anatomical vessels, at least one of the one or more segmentations of plaque, and the FFR information. The computer-implemented method further includes displaying the composite rendering in a single viewport.

A computer-implemented method includes obtaining an image (1202) of one or more anatomical blood vessels of a subject, wherein the image is a segmentation of the one or more anatomical blood vessels from image data acquired using a three-dimensional medical imaging modality. The computer-implemented method also includes obtaining one or more segments of a plaque in the one or more anatomical blood vessels of the subject (1204). The computer-implemented method also includes obtaining fractional flow reserve (FFR) information of the one or more anatomical blood vessels of the subject (1206). The computer-implemented method further includes generating a composite rendering including the image of the one or more anatomical blood vessels, at least one of the one or more segments of the plaque, and the FFR information (1208). The computer-implemented method also includes displaying the composite rendering in a single viewport.

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.

The invention relates to a coronary artery plaque mechanical state evaluation method fusing physical and mechanical information, and belongs to the technical field of medical engineering, and the method comprises the following steps: S1, collecting medical image data and clinical information; s2, constructing a neural network fused with physical mechanics information, designing a loss item based on physical constraint by combining Hooke's law of elastic mechanics and an equilibrium equation, and combining the loss item into a loss function of the neural network; s3, solving the mechanical state of the coronary artery plaque by using a traditional numerical method, obtaining stress-strain related information, pairing the stress-strain related information with the corresponding medical image data and clinical information, and constructing a data set; s4, training, optimizing and testing the neural network fused with the physical and mechanical information by using a data set; s5, coronary artery medical image data to be evaluated and clinical information are input into the trained physical information neural network, and mechanical state evaluation results, including stress distribution and strain characteristics, of the plaques are output.

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

A method and apparatus for identifying a vulnerable coronary plaque based on a multimodal large language model (MM-LLM) are provided. The method includes: acquiring a target 3D coronary computed tomography angiography (CCTA) image and a target straightened curved planar reformation (SCPR) image; acquiring plaque mask information of a blood vessel based on the target SCPR image; acquiring basic plaque data based on the plaque mask information; acquiring fluid dynamics data of each plaque area based on the SCPR image; acquiring basic clinical testing information; standardizing above image data, structural data, and textual data separately, and performing, by a regression mapping network, feature fusion to acquire a first fusion feature; acquiring a trained vulnerable plaque identification LLM; and inputting the first fusion feature into the vulnerable plaque identification LLM to acquire an identification result. The method solve problems of poor single-image feature expression ability and low identification performance in traditional technologies.

The application discloses a coronary artery plaque and stenosis analysis system based on a photon CT pure calcification sequence and belongs to the technical field of coronary arteries. The system comprises a data acquisition sequence generation module, a calcification artifact precision correction module and a plaque and stenosis analysis and diagnosis module. The data acquisition sequence generation module is used for coronary artery scanning based on photon counting CT to obtain photon CT pure calcification sequence image data. The calcification artifact precision correction module is used for the precise correction of coronary artery calcification artifacts of CCTA images to determine a real lumen view without artifact interference. The plaque and stenosis analysis and diagnosis module is used for the analysis and diagnosis of coronary artery plaques and stenosis. The application solves the problem that the existing coronary artery plaques and stenosis cannot be accurately analyzed based on a photon CT pure calcification sequence, thereby leading to low analysis and diagnosis accuracy of coronary artery plaques and stenosis. The application can accurately analyze coronary artery plaques and stenosis based on a photon CT pure calcification sequence and can improve the analysis and diagnosis accuracy of coronary artery plaques and stenosis.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

The present invention relates to the field of cardiovascular medicine and discloses a coronary artery vulnerable plaque detection and analysis system, comprising: a data collection module for collecting multimodal imaging data of the coronary arteries and identifying the coronary artery vascular structure and type; a plaque region identification module for locating the branch openings of the coronary arteries and analyzing multiple types of coronary plaques; a plaque classification detection module for extracting the structural features of multiple types of plaques, identifying vulnerable and stable plaques in the coronary arteries, and establishing a coronary artery plaque classification detection system; a vulnerable plaque assessment module for constructing a coronary artery vulnerable plaque scoring mechanism based on the plaque vulnerability index, plaque composition, and radial wall strain value; and a result feedback module for combining the vulnerable plaque scoring mechanism with the plaque classification detection system to generate coronary artery vulnerable plaque feedback results. The present invention can improve the accuracy of coronary artery vulnerable plaque detection.

There is provided a system for cardiac electromagnetic / magnetic catheterization for diagnosing and treating blood vessels of a patient. The system having at least one electromagnetic intraluminal capsule able to force its way through a narrowing blood vessel, the capsule carrying a camera allowing visualization of blood vessels of a patient. There is a portable electromagnetic tip, where the tip pulls the electromagnetic capsule by electromagnetic force, and when the magnetic tip moves along a body of a patient and pulls the intraluminal electromagnetic capsule along with it towards a narrowing blood vessel visualized by the camera, so that the capsule then treats the narrowing site and clears the blood vessel from coronary plaque. In addition working capsule can replace diseased valve in any cardiac position for either temporary or permanent needs.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The invention discloses a multi-modal image fusion-based coronary plaque stability evaluation system and method, and particularly relates to the technical field of modal image fusion analysis, and the method comprises the steps: extracting depth feature vectors from registered coronary CT, intravascular ultrasound and optical coherence tomography images; constructing a heterogeneous graph by taking the depth feature vectors as nodes, aggregating neighbor node information through a graph convolutional network, and generating enhanced feature representation fused with cross-modal context semantics; a cross-modal attention mechanism is applied to the enhanced features to optimize feature weights, and refined feature vectors are obtained; and finally, outputting a stability classification result by using a classifier, and generating a feature heat map for visual interpretation. According to the method, the multi-modal features are fused by adopting the graph structure, and the decision basis visualization is provided, so that the problems of insufficient fusion, low evaluation accuracy and unexplainable result caused by simple feature splicing in the existing method are solved.

The invention discloses a coronary heart disease plaque prediction method based on a medical specialized multi-scale condition, and the method comprises the steps: carrying out the preprocessing, coronary artery and plaque segmentation and three-dimensional reconstruction of a coronary artery CTA image, and obtaining the registered three-dimensional coronary artery and plaque point cloud data; extracting and fusing spatial geometric features and hemodynamic functional features from the registered data, and fusing and enhancing the spatial geometric features and the hemodynamic functional features with clinical indexes of the patient to generate medical specialized condition guide features; the method comprises the following steps: training a diffusion model guided by a multi-scale condition by taking a space-function fusion feature as a target and taking a medical specialized condition guide feature as a control signal, and reversely recovering a mapping relation of plaque features from noise through learning under the guidance of a medical condition; and by using the trained diffusion model, starting from random noise, generating predicted plaque features at future moments, and restoring the predicted plaque features into a three-dimensional structure. According to the method, through multi-modal feature fusion and innovative model design, accurate, personalized and dynamic prediction of future development of coronary plaques is realized.

This invention presents a cross-modal distillation-coupled coronary plaque classification method, aiming to address the limitations of CTA (Coronary Artery Therapy) due to its low spatial resolution and susceptibility to artifacts, while high-precision IVUS (Intravascular Ultrasound) requires inserting the probe into the vessel, potentially causing discomfort and complications for patients. To combine the advantages of both CTA and IVUS, this invention utilizes artificial intelligence to model the mapping relationship between CTA and IVUS. Through automatic delineation and identification of coronary vessels and plaques, the high-resolution characteristics of IVUS overcome the limitations of CTA, endowing CTA images with the discriminative capabilities of IVUS.

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

The invention discloses a system for analyzing coronary plaque and stenosis through a photon CT pure calcification sequence, and belongs to the technical field of coronary arteries, and the system comprises a data collection sequence generation module which is used for carrying out coronary artery scanning based on photon counting CT, and obtaining photon CT pure calcification sequence image data; the calcification artifact accurate correction module is used for carrying out accurate correction on coronary artery calcification artifacts on the CCTA image and determining a real lumen view without artifact interference; and the plaque and stenosis analysis and diagnosis module is used for analyzing and diagnosing coronary artery plaque and stenosis. According to the method, the problem that the accuracy of analysis and diagnosis of the coronary plaque and stenosis is low due to the fact that the coronary plaque and stenosis cannot be accurately analyzed according to the photon CT pure calcification sequence in the prior art is solved. According to the method, the coronary plaque and stenosis can be accurately analyzed according to the photon CT pure calcification sequence, and the analysis and diagnosis accuracy of the coronary plaque and stenosis can be improved.

The invention provides a segmentation model training method and a coronary artery plaque segmentation method and device, and the method comprises the steps: obtaining a training sample image which is a coronary artery image containing coronary artery plaques; the training sample image is marked, a marked image is obtained, and the marked image is an image obtained by marking a coronary artery wall and a plaque area in the coronary artery wall on the training sample image; and constructing an initial segmentation model, and training the initial segmentation model based on the annotated image by taking the training sample image as an input image to obtain a target segmentation model. According to the method, the defects of original convolution feature extraction are overcome, the problem that plaque segmentation based on gray values or gradients is low in accuracy is solved, automatic plaque segmentation of coronary artery CTA is achieved, the burden of doctors can be relieved, and diagnosis and treatment efficiency can be improved.

The present disclosure belongs to the technical field of medical image analysis, and provides a coronary artery plaque detection method, system, storage medium and electronic device. The method comprises: constructing a cross-sectional plane and an inner and outer wall contour of each center line node; constructing a plurality of ray nodes, determining the actual inner wall radius, outer wall radius and plaque label of each ray node based on the cross-sectional plane and the inner and outer wall contour; generating a feature vector of the ray node based on the one-dimensional radial gray sequence and the geometric correlation characteristics of the ray node; generating a coronary artery graph structure based on all cross-sectional planes and all ray nodes; and taking the coronary artery graph structure and all feature vectors as inputs of an original model, training an inner diameter regression head, an outer diameter regression head and a plaque classification head of the original model under the supervision of the actual inner wall radius, outer wall radius and plaque label, and generating a target prediction model for coronary artery plaque detection. The correlation between plaque thickness and plaque type is improved, and the evaluation of plaque thickness and plaque type is more stable.

A method of analyzing a plaque tissue component based on deep learning, the method including: extracting a plurality of first intravascular ultrasound (IVUS) cross-sectional images into which a first IVUS image that is a preprocedural IVUS image of a patient is divided at predetermined intervals; labeling each of the plurality of first IVUS cross-sectional images by using label indices corresponding to plaque tissue components to form labeled images, performing image conversion to obtain a polar coordinate image through which a distribution of tissue components for each angle is identifiable by performing a coordinate transformation based on the labeled images, extracting a label vector for each angle based on the polar coordinate image, and outputting output data obtained by quantifying the tissue components for each angle by using an artificial intelligence model that is trained by using, as training data, the label vector for each angle.