A vascular assessment apparatus is disclosed. The apparatus is configured to receive medical images of a coronary vessel tree of a subject from a medical imaging device and analyze the medical images to identify vessel segments within the coronary vessel tree. For each identified vessel segment, the apparatus is configured to determine flow rates at each identified vessel segment and calculate an index indicative of vascular function based on the determined flow rates.

An embodiment is a blood flow measurement device that generates blood flow information based on data collected by repeatedly scanning an interested cross section intersecting a blood vessel of an eye fundus using OCT. A data collector scans four or more cross sections intersecting the blood vessel. A first calculator determines a first gradient and second gradient of the blood vessel by analyzing a first data group and second data group among four or more pieces of data corresponding to the four or more cross sections collected by the data collector. A second calculator determines a gradient of the blood vessel at the interested cross section based on the first gradient and the second gradient. A blood flow information generator generates the blood flow information based on the gradient determined by the second calculator and the data collected by repeatedly scanning the interested cross section.

There is provided a speckle measurement apparatus to improve accuracy of flow velocity measurement or the like of particulates such as erythrocytes, the speckle measurement apparatus including an imager that captures scattered light images returned from an object to be measured when the object to be measured is irradiated with coherent light as speckle images, and a controller that determines a measurement area that is the same site of the object to be measured in a plurality of the speckle images captured continuously in time series by the imager by incorporating a displacement amount of a relative positional relationship between the object to be measured and the imager.

In a streakline visualization apparatus, a processing unit calculates, by using an expression including a correction value for correcting an error attributable to accelerated motion of a plurality of grid points represented by position information, time differential values of velocities of fluid on the plurality of grid points at each of the plurality of first time points. The processing unit calculates, based on the velocities and the time differential values of the velocities of the fluid on the plurality of grid points at each of the plurality of first time points, positions of a series of a plurality of particles sequentially outputted from a particle generation source as analysis time progresses at each of a plurality of second time points having a second time interval shorter than the first time interval. The processing unit generates display information about a streakline indicating the series of the plurality of particles.

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.

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.

A system for processing a medical image for personalized brain disease diagnosis and status determination, includes: an image processing unit, which obtains a 3D T1 weighted image, a 2D T2 fluid attenuated inversion recovery (FLAIR) image, a magnetic resonance angiogram (MRA) image, which images only a vessel for checking abnormality of a brain vessel, and a 4D phase-contrast flow image for recognizing a state of a blood flow in a vessel; a complex image analyzing unit, which selects a disease-to-be-diagnosed, sets a brain area according to the selected disease, and analyzes brain tissue and a brain vessel; and a personalized diagnosis and result output unit, which outputs a brain state, a disease-specific risk degree, a risk of disease, and a disease prediction result through a machine learning algorithm by utilizing an age-specific data DB.

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.

The present invention provides a rapid calculation method and system for a plaque stability index based on a medical image sequence. The system includes an image acquisition module, an image receiving module, an image processing module, a finite element calculation module and a result visualization module. The image acquisition module and the image receiving module are configured to acquire, receive and transmit a dynamic two-dimensional vascular image sequence; the image processing module is configured to acquire a space-transformational displacement field function by taking a local feature or a global image as a registration based on dynamic information of real-time deformation of an artery under a two-dimensional image; and the finite element calculation module is configured to acquire a time-dependent vascular lumen diameter sequence and a contour deformation parameter as well as a mechanical index by calculation performed by virtue of the above-mentioned displacement field function.

Method and apparatus for vertebral artery dissection risk analysis using hemodynamic variable based four dimensional magnetic resonance flow imaging, comprising obtaining four-dimensional phase-contrast magnetic resonance imaging data, performing pre-processing of the four-dimensional phase-contrast magnetic resonance imaging data, obtaining at least one blood hemodynamic marker from the four-dimensional phase-contrast magnetic resonance imaging data, classifying the at least one blood hemodynamic marker as a hemodynamic predictor of vertebral artery dissection, and creating a comprehensive risk evaluation of vertebral artery dissection using the hemodynamic predictor.