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 analysing 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.

A network is machine trained to estimate flow by spatial location based on input of anatomy information. A medical scan of tissue may be used to generate flow information without the delay or difficulty of performing a medical scan configured for flow imaging or CFD. Anatomy imaging is used to provide flow estimates with the speed provided by the machine-learned network.

Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

An ophthalmic imaging apparatus of an embodiment includes a data acquisition unit, a blood vessel enhanced image forming unit, and a blood vessel gradient distribution determination unit. The data acquisition unit is configured to acquire a three dimensional data set of a fundus of a subject's eye using optical coherence tomography (OCT). The blood vessel enhanced image forming unit is configured to form a blood vessel enhanced image based on the three dimensional data set. The blood vessel gradient distribution determination unit is configured to determine a blood vessel gradient distribution that shows gradients of blood vessels at a plurality of locations in the fundus, based on the blood vessel enhanced image.

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.

An apparatus that senses temperature from a digital infrared sensor is described. A digital signal representing a temperature without conversion from analog is transmitted from the digital infrared sensor received by a microprocessor and converted to body core temperature by the microprocessor.

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.

A method comprises: inputting contrast enhancement data for at least one organ of a patient, at least one patient attribute of the patient, and a test bolus data or bolus tracking data to a regressor; receiving a contrast agent administration protocol from the regressor, based on the contrast enhancement data, the at least one patient attribute and the test bolus or bolus tracking data; and injecting a contrast agent into the patient according to the received contrast agent administration protocol.

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 and system for correction of decorrelation tail artifacts in optical coherence tomography (OCT) angiography volumetric data defines a movable target subvolume within the OCT-A volumetric data. The target subvolume is axially moveable within the OCT-A volumetric data in discrete axial steps. At each axial step, a reference subvolume corresponding to a depth location in the OCT A volumetric data is defined axially offset from the target subvolume. The reference subvolume may be defined within the OCT A volumetric data, or defined within a different (previously corrected) OCT-A volume. Irrespective, corrected OCT-A data that corrects for decorrelation tail artifacts in the target subvolume is defined using information in the reference subvolume and information in the target subvolume.