Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The present invention relates to a device (1) for fractional flow reserve determination, the device (1) comprising: a model source (10) configured to provide a first three-dimensional model (3DM1) of a portion of an imaged vascular vessel tree (VVT) surrounding a stenosed vessel segment (SVS) and configured to provide a second three-dimensional model (3DM2) of a pressure wire insertable into the vascular vessel tree (VVT); and a processor (20) configured to calculate a first blood flow (Q1) through the stenosed vessel segment (SVS) with the pressure wire (PW) inserted into the vascular vessel tree (VVT) based on the first and the second three-dimensional model and to calculate a second blood flow (Q2) through the stenosed vessel segment (SVS) without the pressure wire (PW) inserted into the vascular vessel tree (VVT) based on the first three-dimensional model (3DM1) and to determine a first fractional flow reserve value (FFR1) to be measured with the pressure wire (PW) inserted into the vascular vessel tree (VVT) based on the first blood flow (Q1) and to determine a second fractional flow reserve value (FFR2) to be measured without the pressure wire (PW) inserted into the vascular vessel tree (VVT) based on the second blood flow (Q1).

An apparatus and method of generating a 3D ultrasound image includes acquiring a 3D volumetric data set corresponding to a 3D imaging volume of an ultrasound probe in a 3D detection volume; acquiring a position of the ultrasound probe with respect to the 3D detection volume; acquiring a position of an interventional medical device with respect to the 3D detection volume; determining a position of the interventional medical device relative to the 3D imaging volume of the ultrasound probe; determining an interventional medical device-aligned plane that intersects with a longitudinal axis of the interventional medical device; extracting a texture slice from the 3D imaging volume for a corresponding interventional medical device-aligned plane positional and rotational orientation; mapping the texture slice onto the interventional medical device-aligned plane; and rendering the interventional medical device-aligned plane as a 3D ultrasound image and displaying the rendered 3D ultrasound image on a display screen.

An X-ray-diagnostic apparatus generates time-series first vessel images corresponding to a first direction, and generates second vessel image corresponding to a second direction. The apparatus generates third vessel images corresponding to the second direction, by transforming more than one piece out of the first vessel images based on a blood vessel shape in one of the first vessel images and a blood vessel shape in at least one of the second vessel image, the third vessel image corresponding to respective time phases of the first vessel images. The apparatus generates first color image corresponding to the first direction by using more than one piece out of the first vessel images, and generates second color image corresponding to the second direction by using more than one piece out of the third vessel images. The apparatus displays a stereoscopic image based on the first color-image and the second color image

An X-ray image processing device for providing segmentation information with reduced X-ray dose that includes an interface unit, and a data processing unit. The interface unit is configured to provide a sequence of time series angiographic 2D images of a vascular structure obtained after a contrast agent injection. The data processing unit is configured to determine an arrival time index of a predetermined characteristic related to the contrast agent injection for each of a plurality of determined pixels along the time series, and to compute a connectivity index for each of the plurality of the determined pixels based on the arrival time index. The data processing unit is configured to generate and provide segmentation data of the vascular structure from the plurality of the determined pixels, wherein the segmentation data is based on the connectivity index of the pixels.

A method and apparatus for selecting (i) an imaging angle with minimized foreshortening and/or overlap of a target region from an existing angiographic image and/or (ii) selecting an imaging angle for new images so that foreshortening and/or overlap are minimized. A viewing angle cost function is determined that defines optimal viewing angles at least with respect to minimizing foreshortening of the target region. Using the cost function, an image may be selected from among a set of images, which potentially does not match the optimal imaging angle due to the optimal imaging angle having a high cost as a result of overlapping vascular features. The selected image may have an imaging angle that corresponds to a lower cost due to less overlap compared to the optimal imaging angle.

A method includes for non-invasively determining an instantaneous wave-free ratio metric includes receiving electronically formatted image data generated by an imaging system. The image data includes voxels with intensities representative of a vessel with a stenosis. The method further includes computing peripheral resistances of outlets of the vessel from the image data. The method further includes calculating a stenosis resistance of the stenosis between an inlet of the vessel inlet and the outlets of the vessel based on a set of boundary conditions and a computational fluid dynamics algorithm. The method further includes calculating the instantaneous wave-free ratio metric. The metric is a numerical value, based on the stenosis resistance and generating a signal indicative of the calculated instantaneous wave-free ratio metric.

A system for the detection of blood vessels includes an image sensor coupled to generate video data including a sequence of images of the blood vessels, and a heart rate monitor to measure a heart rate of a patient and to generate heart rate data. A controller is coupled to the image sensor to receive the video data, and coupled to the heart rate monitor to receive the heart rate data. The controller includes logic that when executed by the controller causes the controller to perform operations including isolate localized motion of the blood vessels in the video data using the heart rate data. The controller also computes a blood vessel mask (that includes differences between the video data and the localized motion of the blood vessels) and combined video data (that includes the video data and the blood vessel mask).

Methods and systems are provided for assessing the presence of functionally significant stenosis in one or more coronary arteries, further known as a severity of vessel obstruction. The methods and systems can implement a prediction phase that comprises segmenting at least a portion of a contrast enhanced volume image data set into data segments corresponding to wall regions of the target organ, and analysing the data segments to extract features that are indicative of an amount of perfusion experiences by wall regions of the target organ. The methods and systems can obtain a feature-perfusion classification (FPC) model derived from a training set of perfused organs, classify the data segments based on the features extracted and based on the FPC model, and provide, as an output, a prediction indicative of a severity of vessel obstruction based on the classification of the features.