Systems and methods for image processing are provided. The systems may include obtaining an initial image relating to a blood vessel. The system may include determining a centerline of the blood vessel based on the initial image. The system may also include determining one or more images to be segmented of the blood vessel based on the centerline and the initial image. The system may also include determining a boundary of the lumen of the blood vessel and a boundary of the wall of the blood vessel in the each image for each of the one or more images. The system may further include analyzing the blood vessel based on the one or more boundaries of the lumen and the one or more boundaries of the wall.

For imaging-based characterization of a perfusion defect in a vessel structure for blood supply of an organ of a patient, medical imaging data is received, wherein the medical imaging data includes energy resolved CT imaging data. A blood stream obstructing object in the vessel structure is detected based on the medical imaging data. A perfusion defect score for a target region of the at least one organ, whose blood perfusion is potentially affected by the obstructing object, is determined depending on the energy resolved CT imaging data.

The present disclosure provides to generate ground truth data for training a machine learning (ML) model to infer pressure information (e.g., a pressure curve, a pressure ratio, or the like) for a cardiac artery from border segmentations generated from images of the cardiac artery. The ground truth data can comprise vessel and/or lumen segmentations for several cardiac arteries and associated pressure information for the cardiac arteries. The vessel and/or lumen segmentations can be generated from images from different image modalities (e.g., IVUS, angiographic, CT, etc.). Further, some of the associated pressure information can be based on measured pressure information (e.g., using a pressure sensing catheter) while other associated pressure information can be derived from the vessel and/or lumen border segmentations using numerical analysis techniques (e.g., CFD, or the like).

The invention generally provides systems and methods for measuring flow propagation velocity from multi-dimensional cardiac imaging. In certain aspects, the invention provides systems and methods for measuring propagation velocity from multi-dimensional cardiac imaging that involve receiving cardiac imaging data; estimating local and instantaneous flow propagation velocity (V.sub.prop) from the cardiac imaging data; and employing the local and instantaneous flow propagation velocity to evaluate cardiac flow propagation.

The present application discloses a blood supply information extraction method and apparatus, an electronic device, and a computer-readable storage medium, where the blood supply information extraction method includes: controlling an ultrasonic probe to emit ultrasonic waves to a target body from a plurality of angles, and obtaining ultrasonic echo data of the target body at the plurality of angles, where the imaging areas of the ultrasonic waves emitted from the plurality of angles in the target body have an overlapping area; for the ultrasonic echo data at each angle, respectively performing a blood supply information extraction to obtain blood supply information of the target body at each angle; obtaining blood supply information of the target body in the overlapping area based on the blood supply information at each angle.

Systems and methods for detecting and classifying retinal microaneurysms. The method including: receiving a time sequence of fluorescein angiography input images; generating a binary map of hyperfluorescent elements in the input images; determining which hyperfluorescent elements in the binary map are microaneurysms, by grading each against a combination of morphological metrics; classifying each of the detected microaneurysms as leaky or not leaky, the classification having: identifying an outer ring mask surrounding the detected microaneurysm in the binary map; identifying parenchyma in the outer ring mask using a fluorescence intensity determination; determining a rate of change of fluorescence intensity of the identified parenchyma over time; and classifying the detected microaneurysm as leaky where the rate of change is positive and not leaky where the rate of change is negative or zero; and outputting the classifications of the detected microaneurysms.

An image acquisition unit acquires a phase contrast image consisting of a plurality of phases, in which a pixel value of each pixel represents a velocity of fluid, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a phase contrast magnetic resonance method. An identification unit identifies a region of the structure in the phase contrast image on the basis of a maximum value of the velocity of the fluid between corresponding pixels in each of the phases of the phase contrast image.

Some embodiments of the present disclosure provide methods and systems for determining a hemodynamic parameter. The method may include: obtaining image data of a subject being acquired in a rest state; obtaining a trained machine learning model; and determining, based on the trained machine learning model, at least one target hemodynamic parameter of the subject. The trained machine learning model may be obtained based on multiple sets of sample image data. Each set of the multiple sets of sample image data may include a first image data and at least one of a second image data or a third image data. The first image data may be acquired in a rest state of a first sample subject, the second image data may be acquired in a hyperemic state of the first sample subject, and the third image data may be acquired in a hyperemic state of a second sample subject including the first sample subject.

A medical image processing apparatus receives a first medical image related to a subject from a medical imaging apparatus. The medical image processing apparatus executes a first analysis process for the first medical image to output a first analysis result. The medical image processing apparatus determines, based on the first analysis result, whether or not a second analysis process is required as an additional analysis process. The medical image processing apparatus establishes, if it has been determined that the second analysis process is required, an image characteristic recommended to be equipped in a second medical image used in the second analysis process in accordance with a processing content of the second analysis process.

Systems and methods for evaluating a proposed valve-in-valve procedure for a patient in which a replacement transcatheter aortic valve will be deployed within a first bioprosthetic aortic valve. The methods include selecting predetermined benchmark measurements of a valve-in-valve combination. Images of anatomy of the patient are received. Anatomical measurements of the first bioprosthetic valve are obtained from the received images. The predetermined benchmark measurements and the anatomical measurements are reviewed. Based, at least in part, upon the review, risks of a valve-in-valve procedure for the patient are evaluated. The methods of the present disclosure can be used on baseline scans of a patient without a first bioprosthetic valve implanted; under these circumstances, dimensions of the first valve are determined by benchmark measurements. Where methods of the present disclosure are used on post-first implant scans, then the dimensions of the first valve are determined from the post-implant scans.