950 results about "Coronary arteries" 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.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Systems and methods for four-dimensional analysis of a patient's coronary arteries and myocardial wall. An example method includes accessing angiographic x-ray images of one or more arteries, with the x-ray images depicting the arteries from respective angles, with each x-ray image being associated with a cardiac phase of cardiac phases, and with the cardiac phases being included in a cardiac cycle. A three-dimensional model of the arteries is generated for each cardiac phase based on a subset of the x-ray images which are associated with the cardiac phase. A four-dimensional representation of the arteries is generated throughout the cardiac cycle based on the three-dimensional models for the cardiac phase. Information associated with the four-dimensional presentation is presented.

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.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

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 disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to a coronary artery fractional flow reserve non-invasive evaluation method fused with hemodynamics, and belongs to the technical field of medical detection, and the method comprises the following steps: obtaining coronary artery CT image data, carrying out preprocessing, and extracting a coronary artery three-dimensional geometric model; constructing a neural network fused with hemodynamics, and adding a residual term of a Navier-Stokes equation and a residual term of a Murray law into a loss function; collecting a real training sample, and generating numerical simulation data as a training data set; s4, training and optimizing the neural network fused with hemodynamics to minimize an error between an output result of the network and an FFR training value, and satisfying constraint conditions of a Navier-Stokes equation and a Murray law at the same time; and S5, processing the coronary artery CT image data by using the trained network model fused with hemodynamics to obtain a predicted FFR value.

An apparatus for synchronizing a three-dimensional model of a patient's coronary arteries with an orientation of a medical imaging device is disclosed. In an example, an apparatus is configured to receive an indication that a three-dimensional model has been rotated. The indication includes a number of degrees of rotation of the three-dimensional model along a roll and / or pitch axis. The processor uses a correlation or transfer function to determine a rotational angulation position of the medical imaging device based on the number of degrees of rotation of the three-dimensional model along the roll and / or pitch axis. The processor transmits a command or instruction to the medical imaging device that is indicative of the desired rotational angulation position, thereby causing the medical imaging device to rotate to the desired position.

A method and apparatus for calculating the blood flow rate in a coronary artery, an electronic device and a storage medium. The method for calculating the blood flow rate in a coronary artery comprises the following steps: S1, acquiring an angiography image of the coronary artery, segmenting the angiography image of the coronary artery by using deep learning, and obtaining segmented images of a main vessel (S1); S2, calculating the length of the main vessel in each segmented image frame on the basis of the segmented images of the main vessel (S2); and S3, obtaining the blood flow rate in the main vessel on the basis of the calculated change of the lengths of the main vessel with time (S3). By using the method and apparatus for calculating the blood flow rate in a coronary artery and the electronic device, the automation of the calculation of the blood flow rate in a coronary artery is achieved, the calculated blood flow rate in the coronary artery is more accurate, and the calculation method is simple.

The invention relates to the field of cardiovascular medicine, and discloses a coronary artery vulnerable plaque detection and analysis system which comprises a data collection module used for collecting multi-mode imaging data of a coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery, and a plaque area identification module used for positioning the branch opening position of the coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery. Multi-class plaques of coronary arteries are separated out; the plaque classification detection module is used for extracting structural features of multiple categories of plaques, identifying vulnerable plaques and stable plaques in the coronary artery and setting a plaque classification detection system of the coronary artery; the vulnerable plaque evaluation module is used for constructing a coronary artery vulnerable plaque scoring mechanism according to the plaque vulnerability index, the plaque component and the radial wall strain value; and the result feedback module is used for generating a vulnerable plaque feedback result of the coronary artery in combination with the vulnerable plaque scoring mechanism and the plaque classification detection system. According to the invention, the accuracy of coronary artery vulnerable plaque detection can be improved.

The invention relates to a coronary artery risk prediction method and system for a coronary heart disease patient, and aims to solve the limitation that a traditional static evaluation model is difficult to integrate multi-source dynamic data. Collecting multi-source heterogeneous data of a patient from a hospital information system, a gene database and wearable equipment; the method comprises the following steps: respectively extracting key features through a special processing module: analyzing unstructured text and image plaque features by adopting a deep learning model, calculating a multi-gene risk score, capturing a physiological parameter attenuation trend by utilizing time sequence analysis, constructing a three-stage cascade integration model, screening high-contribution features at the first stage, and constructing a three-stage cascade integration model; the secondary stage integrates space-time dynamic characteristics through an attention mechanism network, the final stage integrates static and dynamic factors to generate a dynamic risk score, a visual risk trend chart and a high-risk factor thermodynamic diagram are output, and real-time early warning and linkage are performed through a medical terminal to generate a personalized intervention scheme. According to the method, collaborative analysis and dynamic risk tracking of multi-dimensional data are realized, and the early warning capability and the clinical decision-making efficiency are remarkably improved.

The invention discloses a blood vessel continuous segmentation method based on a graph network, and relates to the technical field of blood vessel continuous segmentation, multi-scale texture features based on coronary artery influence and topological structure features of blood vessels are fused, and correlation among different features is enhanced through an attention mechanism; segmenting the fused multi-scale texture features influenced by the coronary artery and topological structure features of the blood vessel, extracting multi-scale features, and reducing the resolution; gradually recovering the resolution by using a decoder to obtain a segmentation result, and optimizing the network weight by using an error between the segmentation result and a real label; and applying the trained segmentation network to test data to obtain a three-dimensional segmentation result of the coronary artery, and evaluating the accuracy of the segmentation result by comparing the difference between a predicted value and a true value to obtain connection constraint loss. The problem that a three-dimensional blood vessel structure is difficult to extract by a general medical segmentation model is solved, so that a blood vessel segmentation result is more continuous and more accurate.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

A method is provided for optimizing workflow for a vascular intervention based on a three-dimensional dataset of a vessel, particularly a coronary artery, acquired from a 3D imaging modality, which involves:determining vessel centerline;performing segmentation in the dataset to identify the vessel lumen;performing plaque segmentation in the dataset to identifying the plaque in the vessel wall;defining a parameter related to the plaque severity in the vessel wall; andcreating a two-dimensional image spanning the length of the segmented vessel to illustrate both the plaque severity parameter and its spatial distribution in relation to the vessel wall circumference using the position of points along the centerline in the segmented vessel as primary axis.Other aspects are described and claimed.

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 relates to a hypertrophic obstructive cardiomyopathy virtual resection operation curative effect evaluation method based on individualized computational fluid mechanics, which can give visual postoperative hemodynamic indexes, predict postoperative curative effects, find an optimal resection scheme and realize preoperative accurate intelligent evaluation. Comprising the following steps of: acquiring coronary artery enhanced computed tomography image data, acquiring a coronary artery enhanced computed tomography image of a patient, and outputting and storing the coronary artery enhanced computed tomography image in a DICOM (Digital Imaging and Communications in Medicine) format; preprocessing image data, and segmenting and reconstructing a left ventricle-outflow tract structure three-dimensional model; setting a virtual excision scheme at the fleshy part; constructing a surgical resection model, and importing the model into finite element analysis software for solving; and the virtual postoperative curative effect is visualized. Verification and curative effect prediction of a patient excision scheme can be realized, and the operation success rate of a hypertrophic obstructive cardiomyopathy patient is improved.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method combining a convolutional neural network and a graph network, and the method comprises the following steps: forming a data set CoronarySet by using coronary artery CT angiography three-dimensional image data collected by a computed tomography (CT) device, and randomly dividing the data set CoronarySet into a training set CoronarySet 1 and a test set CoronarySet 2; the method comprises the following steps of: segmenting data of a training set CoronarySet1 into a plurality of patches; inputting the patchings into a double-branch parallel encoder to obtain a multi-layer coronary artery texture feature map and a multi-layer coronary artery topological feature map; constructing a feature fusion module, and fusing the texture feature maps and the topological feature maps of different layers based on an attention mechanism module to obtain fused blood vessel features; shallow layer features of a three-dimensional convolution module and fused blood vessel features are sequentially input into each layer of a segmentation decoder, a three-dimensional blood vessel structure can be extracted, and a blood vessel segmentation result is more continuous and more accurate.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

The disclosure provides methods of reducing the risk of a major adverse cardiovascular event (MACE) in a subject having previously experienced acute coronary syndrome (ACS) and for reducing risk of a MACE in a patient who is diagnosed with coronary artery disease and has blood high-sensitivity C- reactive (hs-CRP) of >2.0 mg / L. The methods include administering to the subject an anti-oxLDL antibody. The anti-oxLDL antibody may be administered to the subject alone or in combination with one or more additional therapeutic agents, such as one or more lipid lowering agents.

An aorta intervention catheter device for heart failure comprises a catheter and a bag body arranged at one end of the catheter, an elastic diaphragm is arranged in the bag body, an inner cavity of the bag body is divided into a blood chamber and an air chamber by the elastic diaphragm, the blood chamber is communicated with the catheter, an exhaust assembly is arranged on the bag body, and the air chamber is connected with an air source connector arranged on the bag body; the other end of the catheter is a proximal end, first guide films which are staggered at intervals and inclined towards one side of the proximal end and second guide films which are inclined towards a distal end are arranged on the inner wall of the catheter, and first guide holes and second guide holes which correspond to the first guide films and the second guide films and are communicated with an inner cavity of the catheter are respectively formed in the outer wall of the catheter; the proximal side of the second guide film is provided with at least one set of third guide films which are fixed to the inner wall of the catheter, incline towards the distal end and are tightly attached to each other to be closed. The aorta is directly intervened to enter the heart, and coronary artery blood perfusion is increased in the diastolic period; the left ventricular post-load is reduced in the systolic period, and the heart failure can be more directly treated.

The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and / or estimate a metric associated with the presence, non-presence, severity, and / or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.

A polygenic risk score (PRS) for coronary artery disease, a construction method therefor, and an application thereof in combination with clinical risk assessment. The present invention first provides an application of a reagent for detecting individual information in preparation of a detection device for assessing the onset risk of the coronary artery disease, wherein the individual information comprises 311 CAD-related single nucleotide polymorphic sites, and the individual information preferably also comprises one or more of BP, BMI, DM, TC and Stroke-related single nucleotide polymorphic sites. The present invention further provides a method for constructing a comprehensive metaPRS for the coronary artery disease. In the present invention, the PRS and the conventional clinical risk factor score are further integrated, such that re-layering of the onset risk of the coronary artery disease can be realized. The present invention is of great significance to primary prevention of the coronary artery disease.

A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The invention relates to the technical field of medical information, in particular to a coronary artery stenosis intelligent diagnosis system and method based on deep learning, and realizes efficient and accurate diagnosis through data acquisition, marking, convolutional neural network model construction, evaluation and acceleration modules. The system preprocesses the contrast image, marks the narrow position and degree, constructs a convolutional neural network model combined with an attention degree graph, and optimizes parameters to improve the accuracy; through parallel processing, the diagnosis time is shortened, the problems of an existing diagnosis method are systematically and innovatively solved, and the method has high accuracy, high efficiency and good interpretability and is expected to become an important clinical auxiliary tool for early screening and emergency diagnosis of the coronary heart disease.

The invention discloses a liver transplantation perioperative concurrent myocardial ischemia risk assessment method and system, and the method comprises the steps: obtaining coronary artery image data of a liver transplantation patient, carrying out the three-dimensional reconstruction according to the coronary artery image data, and generating a personalized coronary artery geometric model; based on the geometric model of the coronary artery, performing numerical simulation on the flowing condition of blood flow in the coronary artery by utilizing a computational fluid mechanics method, obtaining the speed and pressure distribution of the blood flow of the coronary artery through the numerical simulation, and calculating the pressure ratio before and after the coronary artery stenosis; evaluating the coronary ischemia condition according to the pressure ratio, and generating a fractional flow reserve value and a myocardial ischemia risk evaluation report. The coronary artery blood flow reserve score value is calculated by simulating the coronary artery blood flow conditions under different physiological conditions, so that the myocardial ischemia risk possibly faced by a patient during an operation is comprehensively evaluated, and intra-operation decision support is provided for clinicians.