Abstract: Computer-implemented methods and systems are provided for charactering a property of microvascular tissue that is supplied with blood via a coronary artery under investigation, which involve obtaining an x-ray angiographic image sequence of the coronary artery under investigation acquired while contrast agent flows into and through the coronary artery under investigation. The angiographic image sequence is used to determine a volumetric flow rate for flow through the coronary artery under investigation, which is used to determine an index that represents a property of the microvascular tissue that is supplied with blood via the coronary artery under investigation.

Abstract: Method for quantitative flow analysis of a tree of conduits perfusing an organ from at least two bi-dimensional images, the method comprising the following steps: a) making a 3D reconstruction of at least part of the tree from said at least two bi-dimensional images; b) identifying a segment of interest within the 3D reconstruction either automatically or semi-automatically upon user input; c) making calculations based on the 3D reconstruction to determine geometrical features of the conduits such as diameters, lengths, curvatures, centrelines or the like; d) receiving indication from the user to input a multi-scale functional model of the tree to be considered for the flow analysis and to input the location of the segment of interest within such model; e) adjusting the part of the functional model related to the segment of interest using geometrical features of the 3D reconstruction; f) performing quantitative flow analysis based on the functional model so obtained.

Abstract: Methods and systems for analysis of a tree of conduits perfusing an organ of a patient from a plurality of bi-dimensional images, which involve: a) generating a patient-specific 1D model that includes segments representing conduits of at least part of the tree; b) generating a 3D reconstruction for a subset of the tree; c) identifying a region of interest within the 3D reconstruction corresponding to a stenotic vessel segment; d) using the 3D reconstruction to construct a volumetric mesh corresponding to the region of interest; e) using the volumetric mesh in conjunction with computational fluid dynamic simulations to generate a reduced 3D model for the region of interest; f) adding the reduced 3D model to the patient-specific 1D model as a coupling condition at the position in the 1D model identified in c) to generate a coupled model; and g) performing quantitative flow analysis using the coupled model of f).

Abstract: Methods for correlating intraluminal data with x-ray image data involve providing x-ray image data of a tubular organ, providing intraluminal data of the tubular organ, synchronizing the x-ray image data with the intraluminal data based on a cardiac cycle extracted from an available input signal and/or the x-ray image data and/or the intraluminal data to match x-ray image data with intraluminal data in a specific cardiac phase, and co-registering x-ray image data with intraluminal data by creating a cumulative image at the specific cardiac phase which reconstructs the path followed by an intraluminal wire used for acquiring the intraluminal data to link the intraluminal data to spatial locations of the path followed by the wire within the x-ray image. A corresponding system and computer program are also disclosed. The method and systems can be controlled by one or more computer systems configured with specific executable instructions.

Abstract: Methods and systems are provided for assessing obstruction of a vessel of interest of a patient, which involve obtaining a volumetric image dataset for the vessel of interest. The volumetric image dataset is analyzed to extract data representing axial trajectory of the vessel of interest. A multi-planar reformatted (MPR) image is generated from the volumetric image dataset and the data representing axial trajectory of the vessel of interest; The MPR image is supplied as input to a first machine learning network that outputs feature data that characterizes a plurality of features of the vessel of interest along the axial trajectory of the vessel of interest given the MPR image. Additional data that characterizes at least one additional feature of the vessel of interest along the axial trajectory of the vessel of interest is generated by analysis separate and distinct from the first machine learning network.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: Methods and systems are provided for dynamically visualizing an object of interest that includes part of the vasculature of a patient, which employ a 3D model of the object to generate at least one roadmap that includes information that characterizes properties of the object (such as centerlines, contours, and an image mask. Reference location(s) corresponding to the roadmap(s) are determined for an interventional device used to treat the object. In an online phase, non-contrast-enhanced x-ray image data of the object are obtained and processed to determine location of the interventional device in the image data, and a particular roadmap is selected or accessed. The reference location corresponding to the particular roadmap and the determined location of interventional device are used to transform the particular roadmap. A visual representation of the transformed roadmap is overlaid on the image data for display.

Abstract: A method for providing guidance in Cardiac Resynchronization Therapy (CRT) comprising: a) providing image data sets of the heart of a patient; b) creating a three-dimensional (3D) anatomical model of the cardiac structures; c) calculating myocardium infarct scar distribution; d) calculating heart mechanical activation data; e) superimposing the scar distribution and the mechanical activation data on the 3D anatomical model to obtain a 3D roadmap model including anatomical geometry and mechanical activation data. A corresponding device and computer program are also disclosed.

Abstract: Methods are provided for dynamically visualizing information in image data of an object of interest of a patient, which include an offline phase and an online phase. In the offline phase, first image data of the object of interest acquired with a contrast agent is obtained with an interventional device is present in the first image data. The first image data is used to generate a plurality of roadmaps of the object of interest. A plurality of reference locations of the device in the first image data is determined, wherein the plurality of reference locations correspond to the plurality of roadmaps. In the online phase, live image data of the object of interest acquired without a contrast agent is obtained with the device present in the live image data, and a roadmap is selected from the plurality of roadmaps. A location of the device in the live image data is determined.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow ve

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.

Abstract: A method for delineating a ventricle from MRI data relating to the heart of a patient, the method comprising: a) providing a contrast-enhanced cine MRI dataset; b) providing one or more additional MRI datasets; c) segmenting one or more features on the additional MRI dataset or datasets; d) mapping the segmented features to the contrast-enhanced cine MRI dataset; and e) using the segmented features as mapped in step d) to assist segmentation of the ventricle on the contrast-enhanced cine MRI dataset. A corresponding device and computer program are also disclosed.

Abstract: Method and systems are provided for characterizing blood flow in an atrioventricular valve of the human heart, the atrioventricular valve connecting an atrium with a corresponding ventricle of the heart, the ventricle being fluidly coupled to a particular vessel that transports blood outside the ventricle blood, which involve obtaining image data of the heart and identifying contours of the atrium and a region within the particular vessel within the image data. A time-density curve for the atrium can be calculated from the contour of the atrium and densitometric image data derived from the image data. A time-density curve for the region of the particular vessel can be calculated from the contour of the vessel region and the densitometric image data. Data that characterizes at least one regurgitation fraction related to the atrioventricular valve can be calculated from such time-density curves.

Abstract: Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

Abstract: 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 analyzing 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.

Abstract: Methods for correlating intraluminal data with x-ray image data involve providing x-ray image data of a tubular organ, providing intraluminal data of the tubular organ, synchronizing the x-ray image data with the intraluminal data based on a cardiac cycle extracted from an available input signal and/or the x-ray image data and/or the intraluminal data to match x-ray image data with intraluminal data in a specific cardiac phase, and co-registering x-ray image data with intraluminal data by creating a cumulative image at the specific cardiac phase which reconstructs the path followed by an intraluminal wire used for acquiring the intraluminal data to link the intraluminal data to spatial locations of the path followed by the wire within the x-ray image. A corresponding system and computer program are also disclosed. The method and systems can be controlled by one or more computer systems configured with specific executable instructions.

Abstract: Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

Abstract: A method for performing flow analysis in a target volume of a moving organ having a long axis, such as the heart, from 4D MR Flow volumetric image data set of such organ, wherein such data set comprises structural information and three-directional velocity information of the target volume over time, the devices, program products and methods comprising, under control of one or more computer systems configured with specific executable instructions: a) deriving from the 4D MR Flow volumetric image data set at least one derived image data set related to the long axis of the moving organ, for example, by using a multi planar reconstruction; b) determining at least one feature of interest in the 4D MR Flow volumetric image data set or in said derived image data set.

Abstract: Methods and systems are described for assessing a vessel obstruction. The methods and systems obtain a volumetric image dataset of a myocardium and at least one coronary vessel, wherein the myocardium comprises muscular tissue of the heart. A three-dimensional (3D) image corresponding to a coronary vessel of interest is created from the volumetric image dataset. Feature data that represents features of both the myocardium and the coronary vessel of interest is generated. At least some of the feature data is determined by a first machine learning-based model based on the 3D image. A second machine learning-based model is used to determine at least one parameter based on the feature data, wherein the at least one parameter represents functionally significant coronary lesion severity of the coronary vessel of interest.