A method includes for non-invasively determining an instantaneous wave-free ratio metric includes receiving electronically formatted image data generated by an imaging system. The image data includes voxels with intensities representative of a vessel with a stenosis. The method further includes computing peripheral resistances of outlets of the vessel from the image data. The method further includes calculating a stenosis resistance of the stenosis between an inlet of the vessel inlet and the outlets of the vessel based on a set of boundary conditions and a computational fluid dynamics algorithm. The method further includes calculating the instantaneous wave-free ratio metric. The metric is a numerical value, based on the stenosis resistance and generating a signal indicative of the calculated instantaneous wave-free ratio metric.

An apparatus and method of generating a 3D ultrasound image includes acquiring a 3D volumetric data set corresponding to a 3D imaging volume of an ultrasound probe in a 3D detection volume; acquiring a position of the ultrasound probe with respect to the 3D detection volume; acquiring a position of an interventional medical device with respect to the 3D detection volume; determining a position of the interventional medical device relative to the 3D imaging volume of the ultrasound probe; determining an interventional medical device-aligned plane that intersects with a longitudinal axis of the interventional medical device; extracting a texture slice from the 3D imaging volume for a corresponding interventional medical device-aligned plane positional and rotational orientation; mapping the texture slice onto the interventional medical device-aligned plane; and rendering the interventional medical device-aligned plane as a 3D ultrasound image and displaying the rendered 3D ultrasound image on a display screen.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

A blood flow analysis device includes a blood vessel region extraction unit that extracts a blood vessel region from a three-dimensional medical image of a subject including a blood vessel, and a blood flow simulation unit that obtains a line resistance by multiplying a resistance value corresponding to a branch blood vessel branching out of the blood vessel region by a surface area of each of preset sections into which the blood vessel region is divided in an extension direction, generates a vascular network model by applying the line resistance to each node of a surface of the blood vessel region, and performs simulation of a blood flow by using the vascular network model.

The present invention relates to methods for assessing or obtaining an indication of vascular pressure associated with organs or visceral tissues of the body by using MRI imaging methods. The invention particularly relates to methods for assessing or obtaining an indication of portal hypertension using Magnetic Resonance T1, or T1 and T2* relaxometry, and T1, T2 and/or T2* mapping of the liver or spleen.

A medical image processing method of determining a fractional flow reserve through a stenosis of a coronary artery from medical image data is disclosed. The medical image data comprises a set of images of a coronary region of a patient. The coronary region includes the stenosis. The method comprises: reconstructing a three dimensional model of a coronary artery tree of the patient from the medical image data; determining stenosis dimensions from the three dimensional model of the coronary artery tree of the patient; simulating blood flow in the three dimensional model of the coronary artery tree of the patient to determine modeled flow rates; using an analytical model depending on the stenosis dimensions to predict a modeled pressure drop over the stenosis from the modeled flow rates; and determining the fractional flow reserve through the stenosis from the modeled pressure drop.

Methods and systems for suppressing shadowgraphic flow projection artifacts in OCT angiography images of a sample are disclosed. In one example approach, normalized OCT angiography data is analyzed at the level of individual A-scans to classify signals as either flow or projection artifact. This classification information is then used to suppress projection artifacts in the three dimensional OCT angiography dataset.

A medical diagnostic system includes an image processor to generate a differential frame from two consecutive medical images of the heart based on difference in pixel-by-pixel brightness of the consecutive images. A brightness segmentation image having a plurality of brightness zones is generated based of the differential frame. From the brightness zones, a turbulence index is calculated based on the iso-contour and the area in each brightness zone. The turbulence index is a quantitative representation of a degree of turbulence in the blood flow which can be used to generate a prognosis and/or diagnosis of cardiac function.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of virtual revascularization of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Frames of a video frame sequence capturing one or more skin regions of a body are provided to a first neural network. The first neural network generates respective appearance representations based on the frames. An appearance representation generated based on a particular frame is indicative of a spatial distribution of a physiological signal across the particular frame. Simultaneously with providing the frames to the first neural network, the frames are also provided to a second neural network. The second neural network determines the physiological signal based on the frames. Determining the physiological signal by the second neural network includes applying the appearance representations, generated by the first neural network, to outputs of one or more layers of the second neural network to emphasize regions, in the frames, that exhibit relatively stronger presence of the physiological signal and deemphasize regions, in the frames, that exhibit relatively weaker presence of physiological signal.