The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

An image processing apparatus, comprises an information acquiring unit that acquires three-dimensional data representing characteristic information on an object at a plurality of voxels; a shape information acquiring unit that acquires information on a surface shape of the object; a distance calculating unit that calculates, for each of the voxels, a distance between a surface of the object and a position inside the object corresponding to the voxel, based on the information on the surface shape; a filtering unit that performs, for each of the voxels, filtering processing, including blur processing in accordance with the calculated distance; and an image generating unit that generates a two-dimensional image, based on the three-dimensional data after the filtering processing.

A method for determining an arterial pulse wave velocity representative of a health condition of a blood vessel includes receiving an image data set comprising a plurality of images of a subject, from an imaging modality. The method also involves determining a blood vessel region in an image from the plurality of images. The method further includes determining a plurality of cross-sectional area values of a blood vessel at a plurality of locations in the blood vessel region, corresponding to a plurality of phases of a cardiac cycle of the subject and determining a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values. The method also includes determining a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values and determining the arterial pulse wave velocity based on the hemodynamic model.

An image registration device includes: an image acquisition unit that acquires a plurality of images; a divided region setting unit that sets divided regions by dividing each registration processing target of the plurality of images; a pixel value conversion method determination unit that determines, for each set of divided regions set at the same position of the respective registration processing targets, a pixel value conversion method for each divided region based on a pixel value of each divided region of the set; a pixel value conversion unit that performs pixel value conversion within each divided region of the set of divided regions using the determined pixel value conversion method for each divided region; and a registration processing unit that performs registration processing on the plurality of images subjected to pixel value conversion.

The present disclosure relates to a device, a system, and a computer-readable medium for calculating vessel flow parameters based on angiography. In one implementation, the device includes a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: selecting a plurality of template frames from the angiographic images to generate a 3D model for a vessel; determining a start frame and an end frame in the plurality of angiographic images showing a contrast filling process; determining corresponding locations of front ends of the contrast in the start frame and the end frame in the 3D model of the vessel; calculating a vessel volume between the determined locations of the front ends in the 3D model; and determining an average blood flow rate based on the calculated volume, and a time interval between the start frame and the end frame.

An ultrasound diagnosis apparatus according to an embodiment includes an image generating unit, a specifying unit, and an obtaining unit. The image generating unit is configured to generate images in a time series on the basis of a result of a scan performed on a scan region. The specifying unit is configured to specify the position of a moving member included in the scan region, with respect to each of the images in the time series. The obtaining unit is configured to obtain movement information of the moving member on the basis of the positions of the moving member and to obtain a moment of first or higher order related to the movement information of the moving member, with respect to at least a part of the scan region.

The present disclosure provides a method and system for processing multi-modality images. The method may include obtaining multi-modality images; registering the multi-modality images; fusing the multi-modality images; generating a reconstructed image based on a fusion result of the multi-modality images; and determining a removal range with respect to a focus based on the reconstructed image. The multi-modality images may include at least three modalities. The multi-modality images may include a focus.

Described herein are systems, methods, and computer-readable medium for detecting a perfusion abnormality of a subject. In one embodiment, a method includes the following: obtaining, by dynamic radiography, imaging data for a dynamic series of a plurality of x-ray images that include areas of a subject corresponding to pulmonary vasculature; identifying, based on the imaging data, a dynamic signal corresponding to changing blood volume during the cardiac cycle of the subject; decomposing the dynamic signal into periodic components in frequency space; identifying, from the periodic components in frequency space, signals oscillating at the heart rate of the subject; generating, based on the identified signals oscillating at the heart rate of the subject, a perfusion map representation corresponding to pulmonary tissue perfusion in the subject; and detecting, based at least in part on the generated perfusion map representation, a perfusion abnormality of the subject.

An automated method for analysing capillaries in a plurality of images acquired from a subject. The method comprising the steps of: a) acquiring the plurality of images; b) generating a plurality of capillary candidate maps for each of said images, each capillary candidate map comprising one or more regions of interest for each of said images, wherein for each image, each of the respective capillary candidate maps is generated by comparing said image to a different criterion; c) combining said capillary candidate maps to generate a combined capillary candidate map; d) using a first neural network to determine a respective location of one or more detected capillaries in said combined capillary candidate map; e) using a second neural network to determine an optical flow of said detected capillaries; and f) extracting one or more capillary parameters using said detected capillaries and/or said determined flow.

Images where the vascular walls are automatically divided are obtained by obtaining two MRI images that reflect different properties of the blood vessel and obtaining the difference between the two images. This can image both the vascular inner and the outer walls, thereby obtaining the exact size of the blood vessel, and the thickness between the vascular inner and outer walls. Therefore, it is possible to stably perform the operation using a stent with an accurate size during the stent procedure.