Z maps combined with a standardized stimulus in the form of a targeted arterial partial pressures of carbon dioxide provide suprisingly enhanced images for the assessment of pathological CVR. For example, the z-map assessment of patients with known steno-occlusive diseases of the cervico-cerebral vasculature showed an enhanced resolution of the presence, localization, and severity of the pathological CVR. Z-map have been found to be useful to reduce the confounding effects of test-to-test, subject-to-subject, and platform-to-platform variability for comparison of CVR images showing the importance of combining this analysis with the standardized stimulus.

A medical image processing apparatus including processing circuitry configured to: obtain from medical imaging measurements, observations of one or more vector or tensor valued fields as projected from one or more 2D acquisition planes; use an optimisation procedure to determine from the observations a superset of 3D fields (which may be scalar, vector, or tensor) via a solution ansatz constrained by a system of partial differential equations, and output the plurality of these fields.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A medical image processing apparatus according to an embodiment includes processing circuitry configured to acquire geometric data indicating a position of a region of interest set on a first blood vessel image, specify a corresponding region in a second blood vessel image that corresponds to the region of interest on the basis of the geometric data, and acquire information on the corresponding region.

A method for determining a predicted risk level of a clinical endpoint for a predetermined time period for a patient is provided by the present disclosure. The method includes receiving video frames of a heart, the video frames being associated with the patient, receiving electronic health record data including a number of variables associated with the patient, providing the video frames and the electronic health record data to the trained neural network, receiving a risk score from the trained neural network, and outputting a report based on the risk score to at least one of a display or a memory.

Disclosed is a near-infrared imaging optode (200) and associated system and method for transmitting near-infrared radiation into and/or receiving near-infrared radiation from a subjects head. The optode (200) comprises a plurality of resilient optical fibres (201) arranged to transmit and/or receive corresponding near-infrared radiation. The plurality of optical fibres (201) each comprise a distal end (203) arranged to make contact with a subjects head. The distal ends (203) of the plurality of optical fibres (201) are movable relative to each other.

A computerized system and method of modeling myocardial tissue perfusion can include acquiring a plurality of original frames of magnetic resonance imaging (MRI) data representing images of a heart of a subject and developing a manually segmented set of ground truth frames from the original frames. Applying training augmentation techniques to a training set of the originals frame of MRI data can prepare the data for training at least one convolutional neural network (CNN). The CNN can segment the training set of frames according to the ground truth frames. Applying the respective input test frames to a trained CNN can allow for segmenting an endocardium layer and an epicardium layer within the respective images of the input test frames. The segmented images can be used in calculating myocardial blood flow into the myocardium from segmented images of the input test frames.

The present disclosure relates to a method for vascular imaging and determining dynamic vascular parameters of blood flow. According to an embodiment, the present disclosure relates to an apparatus and method of determining dynamic vascular parameters of blood flow, comprising acquiring two-dimensional projection images of a vascular region of interest at a predetermined frequency, the vascular region of interest being downstream of a site of vascular administration of a radio-opaque medium, identifying, within the acquired two-dimensional projection images, heterogeneities of the radio-opaque medium, and determining the dynamic vascular parameters of the blood flow based on spatial movements of the identified heterogeneities of the radio-opaque medium. In an embodiment, the predetermined frequency is greater than 100 Hz.

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

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.