9 results about "Coronary arterial tree" patented technology

Systems and methods for vulnerable plaque assessment and outcome prediction in coronary artery disease. Medical imaging data is used to generate a coronary tree model of coronary centerlines of a patient. The coronary tree model includes a plurality of nodes that represent locations in the coronary tree model. Feature embedding associated with each node are determined from a plurality of features derived from the medical imaging data. The feature embeddings are input into a trained graph neural network that is configured to output an assessment at a node level, a segment level, and / or a coronary tree level for vulnerable plaque.

A system (100) for generating a three-dimensional image of a coronary tree (449) includes a memory (151) and a processor (152). The processor (152) is configured to to: obtain a sequence of two-dimensional angiogram images (410) corresponding to a moving heart from a single viewpoint of an imaging device; and generate, using a trained machine learning model (430), a three-dimensional representation (211A) of the coronary tree (449) based on the sequence of the two-dimensional angiogram images (410) and cardiac motion of the moving heart.

A computer-implemented method includes obtaining, via a processing system comprising one or more processors, a three-dimensional (3D) coronary tree segmented from computed tomography (CT) cardiac scan data of a subject, wherein the 3D coronary tree is labeled. The computer-implemented method also includes obtaining, via the processing system, a standardized two-dimensional (2D) coronary base schematic representation of a generic coronary tree. The computer-implemented method further includes modifying, via the processing system, the standardized 2D coronary base schematic representation of the generic coronary tree based on the 3D coronary tree to generate a standardized 2D coronary personalized schematic representation of a coronary tree of the subject.

This application relates to a system, method, and apparatus for image-based analysis of plaque. In some embodiments, the methods described herein can be used for developing treatment plans. Treatment plans may include local treatment, systemic treatment, or both. In some embodiments, the approaches described herein can be used for stent selection. In some embodiments, the approaches described herein can be used for surgical planning, including robot-assisted surgical planning. In some embodiments, the approaches described herein can be used for image normalization. In some embodiments, the approaches described herein can be used to identify a threshold for plaque calcification. In some embodiments, the approaches described herein can be used to identify thin-capsule fibrous atheroma. In some embodiments, the approaches described herein can be used for reconstruction of the coronary artery tree. Some embodiments are intended for risk stratification of coronary artery disease.

A computer-implemented method includes obtaining, via a processing system comprising one or more processors, a three-dimensional (3D) coronary tree segmented from computed tomography (CT) cardiac scan data of a subject, wherein the 3D coronary tree is labeled. The computer-implemented method also includes obtaining, via the processing system, a standardized two-dimensional (2D) coronary base schematic representation of a generic coronary tree. The computer-implemented method further includes modifying, via the processing system, the standardized 2D coronary base schematic representation of the generic coronary tree based on the 3D coronary tree to generate a standardized 2D coronary personalized schematic representation of a coronary tree of the subject.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at the least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ration of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

A medical image processing method for determining a fractional flow reserve of a stenotic lesion through a coronary artery from medical image data is disclosed. The medical image data includes a set of images of a coronary artery region of a patient. The coronary artery region includes the stenotic lesion. The method comprises the following steps: reconstructing a three-dimensional coronary artery tree model of a patient according to medical image data; determining a stenosis lesion size according to the three-dimensional coronary artery tree model of the patient; simulating blood flow in the three-dimensional coronary artery tree model of the patient to determine a model flow; predicting a model pressure drop across the stenotic lesion using an analytical model depending on the stenotic lesion size according to the model flow; a fractional flow reserve through the stenotic lesion is determined based on the model pressure drop.

Systems and methods for vulnerable plaque assessment and outcome prediction in coronary artery disease. Medical imaging data is used to generate a coronary tree model of a coronary centerline of the patient. The coronary artery tree model includes a plurality of nodes representing locations in the coronary artery tree model. A feature embedding associated with each node is determined from a plurality of features derived from medical imaging data. The feature embedding is input into a trained graph neural network configured to output an assessment of vulnerable plaques at a node level, a segment level, and / or a coronary tree level.