A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

A dynamic analysis system includes a hardware processor. The hardware processor: analyzes a dynamic image for a dynamic state of a living body; generates an analysis result image showing the analysis result; determines, for each pixel of the dynamic image or the analysis result image, whether a pixel value is within a predetermined range of values; classifies the pixels into groups according to the determination result; extracts, as each border pixel, a pixel in a group adjacent to a pixel classified into a different group; generates a border between the groups based on the extracted border pixels; superimposes the border on, between the dynamic image and the analysis result image, an image not subjected to the classification, thereby generating a combined image; and causes an output device to output the combined image.

An apparatus of motion amplification to communicate biological vital signs includes a first frequency filter that applies a frequency filter to at least two images, a regional facial clusterial module that is coupled to the first frequency filter and that applies spatial clustering to output of the first frequency filter, a second frequency filter that is coupled to the regional facial clusterial module and that is applied to output of the regional facial clusterial module, thus generating a temporal variation, a vital-sign generator that is coupled to the second frequency filter that generates at least one vital sign from the temporal variation, and a display device that is coupled to the vital-sign generator that displays the at least one vital sign.

A computed tomography (CT)-based clinical decision support system provides fractional flow reserve (FFR) decision support. The available data, such as the coronary CT data, is used to determine whether to dedicate resources to CT-FFR for a specific patient. A machine-learnt predictor or other model, with access to determinative patient information, is used to assist in a clinical decision regarding CT-FFR. This determination may be made prior to review by a radiologist and/or treating physician to assist decision making.

A CT-based clinical decision support system provides coronary decision support. With or without CT-FFR, a machine learnt predictor predicts the clinical decision for the patient based on input from various sources. Using the machine learnt predictor provides more consistent and comprehensive consideration of the available information. The clinical decision support may be provided prior to review of coronary CT data by a radiologist and/or treating physician, providing a starting point or recommendation that may be used by the radiologist and/or treating physician.

The disclosure relates to method of processing three-dimensional images or volumetric datasets to determine a configuration of a medium or a rate of a change of the medium, wherein the method includes tracking changes of a field related to the medium to obtain a deformation or velocity field in three dimensions. In some cases, the field is a brightness field inherent to the medium or its motion. In other embodiments, the brightness field is from a tracking agent that includes floating particles detectable in the medium during flow of the medium.

A method and system for simulating cardiac function of a patient. A patient-specific anatomical model of at least a portion of the patient's heart is generated from medical image data. Cardiac electrophysiology potentials are calculated over a computational domain defined by the patient-specific anatomical model for each of a plurality of time steps using a patient-specific cardiac electrophysiology model. The electrophysiology potentials acting on a plurality of nodes of the computational domain are calculated in parallel for each time step. Biomechanical forces are calculated over the computational domain for each of the plurality of time steps using a cardiac biomechanical model coupled to the cardiac electrophysiology model. The biomechanical forces acting on a plurality of nodes of the computational domain are estimated in parallel for each time step. Blood flow and cardiac movement are computed at each of the plurality of time steps based on the calculated biomechanical forces.

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Methods and systems for angiographic imaging with optical coherence tomography (OCT) are described using ratio-based and angiographic deviation based calculations. In using these calculations to determine motion, arbitrary interframe permutations may be used, post-calculated, non-linear results for projection visualization may be averaged, poor matches may be eliminated on an A-line by A-line basis, windowing functions may be used to improve results, partial spectrums may be used when capturing data, and a minimum intensity threshold may be used for determining which pixels to use.