A system and method for generating reports on perfusion blood volume from computed tomography (CT) data acquired from a subject. The method includes receiving multi-faceted CT data acquired from the subject using one of a multi-energy or polychromatic CT acquisition and deriving an iodine concentration in an artery feeding a volume of interest (VOI) in the multi-faceted CT data. The method further includes determining an effective atomic number of a spatial distribution in the VOL calculating a perfused blood volume of the VOI using the iodine concentration and the effective atomic number, and generating a report of the perfused blood volume of the VOI.

Methods and systems are provided for cardiac computed tomography imaging. In one embodiment, a method comprises reconstructing an image from projection data acquired during a scan with a reconstruction time determined based on a model relating a timing of an event to be imaged to a heart rate measured during the scan. In this way, the timing of a reconstruction may be consistently applied for a series of reconstructions, thereby inherently registering the reconstructions.

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.

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

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.

Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.

A method for determining an intracranial hemodynamic parameter according to embodiments of the present disclosure is provided, which includes determining a three-dimensional a model of a blood vessel based on CT angiographic data, and determining at least one of a boundary condition of each inlet or a boundary condition of each outlet in the three-dimensional vessel model based on CT perfusion imaging data and CT angiographic data.

A neural network is trained for estimating patient hemodynamic data using a plurality of extravascular imaging data sets and a plurality of intravascular imaging data sets that are each co-registered to a corresponding extravascular imaging data set. A plurality of hemodynamic data sets are provided, each hemodynamic data set co-registered with the corresponding extravascular imaging data set. The neural network learns what hemodynamic data to expect for a given intravascular imaging data set. An intravascular imaging event is subsequently performed in which an intravascular imaging element is translated within a blood vessel of the patient to produce one or more intravascular images. The neural network uses its training to predict hemodynamic values corresponding to the one or more intravascular images from the intravascular imaging event, and the one or more intravascular images are outputted in combination with the predicted hemodynamic values.

One or more example embodiments of the present invention relates to a method for providing vessel wall-related data. The method includes receiving spectral computed tomography data of an examination region, the examination region having a vessel; calculating a representation of a vessel wall of the vessel and at least one parameter map of the examination region based on the spectral computed tomography data; calculating the vessel wall-related data based on the representation of the vessel wall and the at least one parameter map of the examination region; and providing the vessel wall-related data.

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 velocity as a function of the centerline distances of g) and h) and a time interval between the start image frame and the end image frame.
A corresponding imaging device and computer program are also disclosed.