Abstract: Method for modelling blood vessels includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels, based on the medical imaging data; generating a three-dimensional reference model of the blood vessels that reflects a state of healthy blood vessels that lack lesions, based on the medical imaging data or based on numerical reconstruction of the personalized model; performing a numerical simulation of blood flow for the same physical and boundary conditions in the personalized model and in the reference model, the simulation comprising determining conditions of blood flow at an inlet to the blood vessels model and calculating blood flow energy for the inlet and all outlets of the blood vessels model; comparing the blood flow energy measured for the personalized model and for the reference model; determining flow energy change indexes of the blood flow in the personalized model and the reference model.

Abstract: A computer-implemented method for modelling blood vessels to support assessment of probability of rupture or damage to the plaque. The method includes steps of: obtaining medical imaging data of the blood vessels; generating a three-dimensional model of the blood vessels, based on the medical imaging data including identifying one or more pathological plaques; performing pre-simulation of the three-dimensional model, establishing boundary conditions and initial conditions for both models for a steady flow of blood and a transient flow of blood; performing a numerical simulation of the transient flow of blood; performing a numerical simulation of the steady flow of blood; and for a selected plaque, identifying geometrical parameters of a surface of the plaque, including shape, curvature, curvature of the major surface, and/or Gauss curvature of the plaque surface. The method may include calculation of Reference Dynamic Pressure (RDP) and Degree of Stenosis (DS).

Abstract: Method for modelling blood vessels includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels, based on the medical imaging data; generating a three-dimensional reference model of the blood vessels that reflects a state of healthy blood vessels that lack lesions, based on the medical imaging data or based on numerical reconstruction of the personalized model; performing a numerical simulation of blood flow for the same physical and boundary conditions in the personalized model and in the reference model, the simulation comprising determining conditions of blood flow at an inlet to the blood vessels model and calculating blood flow energy for the inlet and all outlets of the blood vessels model; comparing the blood flow energy measured for the personalized model and for the reference model; determining flow energy change indexes of the blood flow in the personalized model and the reference model.

Abstract: Method for modelling blood vessels includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels, based on the medical imaging data; generating a three-dimensional reference model of the blood vessels that reflects a state of healthy blood vessels that lack lesions, based on the medical imaging data or based on numerical reconstruction of the personalized model; performing a numerical simulation of blood flow for the same physical and boundary conditions in the personalized model and in the reference model, the simulation comprising determining conditions of blood flow at an inlet to the blood vessels model and calculating blood flow energy for the inlet and all outlets of the blood vessels model; comparing the blood flow energy measured for the personalized model and for the reference model; determining flow energy change indexes of the blood flow in the personalized model and the reference model.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving (101) an x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing (102) the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation (103) by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.

Abstract: A computer-implemented method for modelling blood vessels, that includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels; generating a three-dimensional reconstructed model of the blood vessels that reflects a state of healthy blood vessels that lack lesions; performing a pre-simulation of the reconstructed model; determining absolute or relative indexes of blood flow as a function that compares at least on of pressure, velocity or energy flow between the personalized model and the reconstructed model.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; dividing the CT scan volume into a first set of subvolumes; extracting a region of interest by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to a second set of 3D subvolumes; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; converting the CT scan volume to three sets of two-dimensional slices; extracting a region of interest (ROI) by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks (CNN), each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to three groups of sets of two-dimensional masked slices; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Abstract: A method for determining a significance of a stenosis in a currently examined blood vessel, the method comprising: providing a pre-trained reasoning module (130) that has been trained to output a value of a stenosis significance parameter by means of a training data set comprising a plurality of records of prior clinically examined stenosis cases, each training record comprising data related to dimensional parameters, blood flow parameters and clinical measurement parameters of the prior clinically examined blood vessel containing the stenosis; inputting, to the pre-trained reasoning module (130), an examination record comprising data related to the dimensional parameters of the currently examined blood vessel containing the stenosis and instructing the reasoning module (130) to output the value of the stenosis significance parameter based on the examination record.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving (101) an x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing (102) the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation (103) by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.

Abstract: A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; converting the CT scan volume to three sets of two-dimensional slices; extracting a region of interest (ROI) by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks (CNN), each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to three groups of sets of two-dimensional masked slices; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Abstract: Method for modelling blood vessels includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels, based on the medical imaging data; generating a three-dimensional reference model of the blood vessels that reflects a state of healthy blood vessels that lack lesions, based on the medical imaging data or based on numerical reconstruction of the personalized model; performing a numerical simulation of blood flow for the same physical and boundary conditions in the personalized model and in the reference model, the simulation comprising determining conditions of blood flow at an inlet to the blood vessels model and calculating blood flow energy for the inlet and all outlets of the blood vessels model; comparing the blood flow energy measured for the personalized model and for the reference model; determining flow energy change indexes of the blood flow in the personalized model and the reference model.