An endoscope system includes: an image acquiring unit that acquires a first frame image obtained by photographing a photographic subject and a second frame image obtained by photographing the photographic subject at a timing different from that of the first frame image; an oxygen saturation calculating unit that calculates an oxygen saturation by using the first frame image and the second frame image; a reliability calculating unit that calculates reliability of the oxygen saturation, calculated by the oxygen calculating unit, by using a signal ratio that is a ratio between a pixel value in a first specific wavelength range corresponding to a specific wavelength range of the first frame image and a pixel value in a second specific wavelength range corresponding to the specific wavelength range of the second frame image; and an information amount adjusting unit that adjusts an information amount of the oxygen saturation by using the reliability.

A medical image processing apparatus of an embodiment includes a processing circuitry. The processing circuitry extracts a plurality of valve leaflets of a heart valve from image data of a subject. The processing circuitry measures, with respect to at least one valve leaflet of the valve leaflets, a length of a region at which the valve leaflet is in contact with another valve leaflet, in a predetermined reference direction. The processing circuitry controls a display to display a distribution of the length at each of a plurality of positions on the valve leaflet.

A system and method are provided for determining vascular velocity using non-invasively acquired medical images. The method includes reconstructing CT angiography (CTA) data into a plurality of images of the subject by producing a composite image using the CTA data corresponding to a set of the plurality of view angles, backprojecting each view angle in the CTA data and weighting a value backprojected into at image pixel by an attenuation value of a corresponding pixel in the composite image, and summing backprojected values for each image pixel to produce a CT image of the subject. The method also includes determining a flow direction or a velocity of flow within a vessel, calculating, using the flow direction or velocity, a pressure in the vessel, and generating a quantitative map of the subject indicating the flow direction, velocity, or pressure in the vessel against an image of the subject including the vessel.

The present disclosure relates to a method, an apparatus, and a system for detecting and classifying an ischemic stroke based on a medical image. A medical image based ischemic stroke detecting and type classifying apparatus according to an aspect of the present disclosure includes an acquiring unit which collects images related to a brain of at least one patient; a detecting unit which determines whether the at least one patient is a large vessel occlusion patient, based on the collected image; a determining unit which determines whether a type of the large vessel occlusion is embolism or intracranial atherosclerosis (ICAS), when the at least one patient is a large vessel occlusion patient; and a diagnosing unit which provides treatment direction information which is applied differently according to the determined type of the large vessel occlusion.

In an example, a method can include segmented image volume data for at least one anatomic structure, wherein the segmented image volume data includes a 3-D image volume that includes the at least one anatomic structure. The method can include searching outward from a computed centerline for the at least one anatomic structure to detect a surface of one or more regions of interest of the at least one anatomic structure by evaluating values associated with voxels representing the 3-D image volume that includes the at least one anatomic structure relative to a threshold. The method can further include isolating the one or more regions of interest of the at least one anatomic structure in response to the searching and generating a region of interest model based on the one or more isolated regions of interest.

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Methods and systems suitable for obtaining information related to at least one of: respiration rate, heart rate, respiration rate variability, heart rate variability, temporal characteristics of at least a part of a heartbeat, temporal characteristics of at least a part of a respiration cycle, or a duration of a time interval of propagation of a blood pressure pulse from a first point or area or part of a body to a second point or area or part of the body, in a non-contact fashion.

A system and method is provided for imaging a contrast agent. The system includes a power injector that delivers a contrast agent as a series of boluses using a known period, flow rate, or duration and with a rate of at least one or more separate boluses per cardiac cycle. An x-ray imaging system acquires a reference dataset of the subject before the contrast agent is delivered and acquires an imaging dataset as the series of boluses are delivered to the subject, wherein multiple images are acquired of the subject per bolus. A computer system receives the reference dataset and the imaging dataset from the x-ray imaging system and reconstructs the reference dataset and the imaging dataset using a reconstruction process that removes the subject from the images to generate time-resolved volumetric images of the contrast agent moving within a volume of the subject without the subject.

Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.

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.