1661 results about "Coronary arteries" patented technology

Embodiments of the present disclosure are configured to assess the severity of a blockage in a vessel and, in particular, a stenosis in a blood vessel. In some particular embodiments, the devices, systems, and methods of the present disclosure are configured to assess the severity of a stenosis in the coronary arteries by monitoring fluid flow. In some embodiments, the devices, systems, and methods of the present disclosure receive analog sensor data that includes fluid flow data and digitizes the analog sensor data according to a quadrature sampling rate. A weighted accumulator performs a baseband conversion on the digitized sensor data and may perform other signal processing steps. The processed data is then provided for use in any one of a number of diagnostic assessments.

A noninvasive patient-specific method is provided to aid in the analysis, diagnosis, prediction or treatment of hemodynamics of the cardiovascular system of a patient. Coronary blood flow and pressure can be predicted using a 3-D patient image-based model that is implicitly coupled with a model of at least a portion of the remaining cardiovascular system. The 3-D patient image-based model includes at least a portion of the thoracic aorta and epicardial coronaries of the patient. The shape of one or more velocity profiles at the interface of the models is enforced to control complex flow features of recirculating or retrograde flow thereby minimizing model instabilities and resulting in patient-specific predictions of coronary flow rate and pressure. The invention allows for patient-specific predictions of the effect of different or varying physiological states and hemodynamic benefits of coronary medical interventions, percutaneous coronary interventions and surgical therapies.

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.

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.

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.

Methods and corresponding systems of identifying coronary artery disease. The methods 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 methods also comprise 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 present disclosure provides a method and an apparatus for acquiring a contour line of a blood vessel according to a centerline of the blood vessel. The method comprises: extracting a centerline of a blood vessel according to a two-dimensional coronary artery angiogram image (S100); obtaining an image of a straightened blood vessel according to the centerline of the blood vessel (S200); setting a threshold Dthreshold for a diameter of the blood vessel on the image of the straightened blood vessel (S300); generating a preset contour line of the blood vessel on both sides of a centerline of the straightened blood vessel according to the Dthreshold (S400); making the preset contour line of the blood vessel step-by-step approach the centerline of the straightened blood vessel to acquire a contour line of the straightened blood vessel (S500); projecting the contour line of the straightened blood vessel back onto the image of the centerline of the blood vessel to obtain a contour line of the blood vessel (S600).

The present disclosure provides a method for correcting a resting blood flow velocity on the basis of an interval time between angiogram images, comprising: acquiring, in an angiography state, an average blood flow velocity Vh from a coronary artery inlet to a distal end of a coronary artery stenosis (S100); acquiring a time difference Δt between start times of two adjacent bolus injections of contrast agent (S200); obtaining a correction coefficient K according to the time difference Δt (S300); obtaining a resting blood flow velocity Vj according to the correction coefficient K and the average blood flow velocity Vh (S400), as well as an apparatus configured for implementing the above method. The disclosure obtains the resting blood flow velocity Vj according to the correction coefficient K and the average blood flow velocity Vh.

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 coronary artery patient exercise tolerance treatment management system based on a random control test, and relates to the technical field of coronary artery patient exercise tolerance treatment management, and the coronary artery patient exercise tolerance treatment management system comprises a scheme recommendation module and an effect feedback module, the scheme recommendation module comprises a data acquisition unit, a scheme recommendation unit and an archive management unit; the data acquisition unit comprises a physiological index monitoring device. According to the coronary artery patient exercise tolerance treatment management system based on the random control test, the exercise tolerance improvement condition of a patient in the treatment process is evaluated through the definite physiological indexes, exercise performance indexes and subjective feeling indexes of the effect feedback module; multi-dimensional evaluation is achieved, and the movement effect and the body reaction of the patient can be comprehensively known; the exercise scheme of the patient can be optimized and adjusted according to the evaluation result and the health file of the patient, and a preferred exercise scheme is generated.

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

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.

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 for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising quantifying radiodensity in regions of coronary plaque, quantifying, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining gradients 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 a 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 radiodensity values of the corresponding perivascular tissue.

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 and apparatus for analyzing diagnostic image data are provided in which a plurality of acquisition images of a vessel of interest having been acquired with a pre-defined acquisition method is received at a trained classifying device and classified, by the classifying device, to extract at least one quantitative feature of the vessel of interest from at least one acquisition image of the plurality of acquisition images. The at least one quantitative feature is then output associated with the at least one acquisition image while the acquisition of the diagnostic image data is still in progress and one or more adjustable image acquisition settings are adjusted based on the at least one quantitative feature to optimize the acquisition of the diagnostic image data.

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.

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.

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.

Systems and methods for post-PCI coronary analysis. An example method includes accessing a plurality of angiographic images depicting at least one vessel of a patient's heart, the angiographic images including two or more post-percutaneous coronary intervention (post-PCI) images and one or more diagnostic images, wherein the post-PCI images depict a PCI location in which a PCI is included to revascularize a lesion, and wherein the diagnostic images depict the lesion; receiving, via a user interface presenting the post-PCI images, user input specifying positions of the PCI location in the post-PCI images; determining an index indicative of vascular function based on the angiographic images, wherein the index is derived based on a three-dimensional model of the portion of the patient's heart, and wherein individual portions of the diagnostic images depicting the lesion are masked during generation of three-dimensional model; and presenting, via the user interface, summary information associated with PCI, the summary information including at least the determined index.

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, preferably a computer implemented method, and system are for providing output data associated with a medical imaging dataset of a patient, the medical imaging dataset including image data of a region of an anatomy of a patient including a plurality of coronary arteries. The output data is a significance score associated with a medical imaging data set of a patient. The method, in the most general terms, includes receiving input data, e.g. via a first interface, generating output data by applying algorithmic operations to the input data, and providing the output data, e.g. via a second interface.

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 relates to the technical field of model construction, in particular to a construction method and system of a myocardial infarction patient risk assessment model. The method comprises the following steps: carrying out sign data acquisition and coronary artery angiography image acquisition on a myocardial infarction patient through a medical information system, carrying out electrocardiosignal-vascular movement mapping processing according to the coronary artery angiography image of the myocardial infarction patient, and meanwhile, carrying out blocked electrocardiosignal pre-gradient feedback identification; blocking electrocardio pre-gradient feedback data is obtained; carrying out electrocardiosignal phase offset analysis on the obstructed electrocardiosignal pregradient feedback data, and constructing a myocardial infarction patient risk assessment model to obtain the myocardial infarction patient risk assessment model; and performing automatic monitoring firmware design on the myocardial infarction patient risk assessment model to obtain myocardial infarction patient risk assessment model monitoring firmware. The myocardial infarction patient risk assessment model construction technology is optimized, so that the assessment model construction technology is more perfect.

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.