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.

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.

A method of processing optical coherence tomography (OCT) scans through a subject's skin, the method comprising: receiving at least one OCT scan through the subject's skin, each scan representing an OCT signal in a slice through the subject's skin; processing each OCT scan so as to determine a set of parameters comprising at least a measure of the atrophy of the vascular structure in the epidermis; in which the processing produces a measurement of skin condition dependent upon each of the set of parameters, and the method comprises outputting the measurement of skin condition.

A vascular assessment apparatus is disclosed. The apparatus is configured to receive a stenotic model having measurements of a coronary vessel tree of a subject, the stenotic model created from a set of medical images recorded before the subject underwent a stent procedure. The apparatus is also configured to receive a post-medical image of the coronary vessel tree of the subject after the stent has been placed into the coronary vessel tree and identify vascular features within the post medical image that correspond to vascular features that are provided among the set of medical images. The apparatus is further configured to modify geometrical information for the corresponding vascular features in the stenotic model to create an updated stenotic model from the identified vascular features of the post medical image and calculate an index indicative of vascular function, based, at least in part, on the updated stenotic model.

In an embodiment, a method converts two images to a transform representation in a transform domain. For each spatial position, the method examines coefficients representing a neighborhood of the spatial position that is spatially the same across each of the two images. The method calculates a first vector in the transform domain based on first coefficients representing the spatial position, the first vector representing change from a first to second image of the two images describing deformation. The method modifies the first vector to create a second vector in the transform domain representing amplified movement at the spatial position between the first and second images. The method calculates second coefficients based on the second vector of the transform domain. From the second coefficients, the method generates an output image showing motion amplified according to the second vector for each spatial position between the first and second images.

Systems and methods for classifying abnormalities within optical coherence tomography images of the eye are presented. One embodiment of the present invention is the classification of pigment epithelial detachments (PEDs) based on characteristics of their internal reflectivity, size and shape. The classification can be based on selected subsets of the data located within or surrounding the abnormalities. Training data can be used to generate the classification scheme and the classification can be weighted to highlight specific classes of particular clinical interest.

Various systems and methods for improved OCT angiography imaging are described. An example method of identifying intraretinal fluid in optical coherence tomography (OCT) image data of an eye includes collecting OCT image data using an OCT system. The data includes at least one cluster scan containing OCT image data collected at approximately same set of locations on the sample. A first motion contrast image is generated by applying a first OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. A second motion contrast image is generated by applying a second OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. An image displaying intraretinal fluid in the eye is generated using the first and second motion contrast images and then displayed or stored or a further analysis thereof.

A medical image processing apparatus comprises processing circuitry configured to acquire a first blood vessel image based on X-rays that are irradiated from a first direction and a second blood vessel image based on X-rays that are irradiated from a second direction; determine a corresponding point on the second blood vessel image, which is a point corresponding to a subject point on the first blood vessel image, by using an epipolar line corresponding to the subject point and blood-flow information based on a change of a density of a contrast agent over time at the subject point; and reconstruct a three-dimensional blood vessel image by using information about the subject point and the corresponding point.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: perform error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large medical imaging data sets and metadata.