In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Stenosis information is obtained by obtaining photographic image data (302) from a displayed image of a blood vessel (103, 203) containing the stenosis. Contours of the blood vessel and the stenosis are detected and dimensions are estimated from the photographic image data. A blood vessel model is reconstructed and fractional flow reserve data is calculated using the blood vessel model.

In some embodiments of the present invention, a method of reducing artifacts includes obtaining OCT/OCTA data from an OCT/OCTA imager; preprocessing OCTA/OCT volume data; extracting features from the preprocessed OCTA/OCT volume data; classifying the OCTA/OCT volume data to provide a probability determination data; determining a percentage data from the probability data determination; and reducing artifacts in response to the percentage data.

A method for assessing blood vessel stenosis using image data of a subject is disclosed. The image data represents a vascular structure of the subject. The method comprises: (a) segmenting, from the image data, a vessel segment representing a segment of a blood vessel, (b) obtaining, using the image data, a plurality of two-dimensional images of the vessel segment; said plurality of two-dimensional images representing respective cross-sections of the vessel segment, (c) identifying, for each of the plurality of two-dimensional images, a lumen area comprising lumen pixels representing a lumen of the corresponding cross-section, (d) obtaining a quantitative measure using the lumen areas of successive cross-sections of the vessel segment, and (e) assessing blood vessel stenosis using the quantitative measure. A computer system for performing the above method is disclosed.

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.

Embodiments can provide a computer-implemented method for simultaneous multi-slice pulse wave velocity measurement, the method comprising simultaneously acquiring a plurality of multiple parallel images slices from a medical imaging device; shifting the plurality of image slices through modulation of the line-by-line phase patterns for each slice in the plurality of slices; deriving a plurality of image waveforms from the plurality of slices; measuring a distance between a plurality of imaging planes corresponding to the plurality of image slices; determining, for each of the image waveforms, a time-to marker; determining the temporal shift by calculating the difference between the time-to markers; and computing the pulse wave velocity by dividing the distance between the plurality of imaging planes by the temporal shift.

A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

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.

An apparatus and methodological framework are provided, named perfusion angiography, for the quantitative analysis and visualization of blood flow parameters from DSA images. The parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and T.sub.max, are computed using a bolus tracking method based on the deconvolution of time-density curves on a pixel-by-pixel basis. Individual contrast concentration curves of overlapping vessels can be delineated with multivariate Gamma fitting. The extracted parameters are each transformed into parametric maps of the target that can be color coded with different colors to represent parameter values within a particular set range. Side by side parametric maps with corresponding DSA images allow expert evaluation and condition diagnosis.