An image acquisition unit acquires a phase contrast image consisting of a plurality of phases for each of three spatial directions, in which a pixel value of each pixel represents a velocity of fluid for each of the three directions, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a three-dimensional cine 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.

The present disclosure relates to a method for providing information necessary for assessing severity of coronary artery stenosis, the method includes: administering a contrast agent to a coronary artery; capturing an angiographic image; setting a suspected stenosis area where stenosis is suspected and a proximal area where blood passes before the suspected stenosis area within region of captured image for observing the coronary artery stenosis based on the captured angiographic image; fixing position of the coronary artery where the contrast agent is administered by image processing; and deriving a blood flow velocity ratio, which is a relative ratio of blood flow in the proximal area and the suspected stenosis area, based on time when brightness changes in the proximal area and the suspected stenosis area of the captured image.

Computer-implemented methods and systems are provided for charactering a property of microvascular tissue that is supplied with blood via a coronary artery under investigation, which involve obtaining an x-ray angiographic image sequence of the coronary artery under investigation acquired while contrast agent flows into and through the coronary artery under investigation. The angiographic image sequence is used to determine a volumetric flow rate for flow through the coronary artery under investigation, which is used to determine an index that represents a property of the microvascular tissue that is supplied with blood via the coronary artery under investigation.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.

Methods and systems for estimating fluid flow characteristics are provided. In one embodiment, a method is provided that includes receiving a plurality of images of fluid flow at a plurality of times. The images may be analyzed with a machine learning model to predict one or more physical characteristics of the fluid flow, such as a velocity field, a pressure field, and/or a stress field. A loss measure may be calculated for the physical characteristics based on physical fluid flow constraints, boundary condition constraints, and/or data mismatch constraints. The machine learning model may be updated based on the loss value.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A system for measurements and calculations of a microcirculatory high-velocity blood flow includes a data acquisition module configured to select and acquire microcirculatory blood vessel image data; a storage module configured to store the acquired microcirculatory blood vessel image data; a velocity measurement module configured to measure a traveling distance and a traveling time of red blood cell (RBC), white blood cell (WBC), or plasma particles in a blood vessel sample and calculate a ratio of the traveling distance to the traveling time to obtain a blood flow velocity; and a high-velocity blood flow index module configured to determine an index level for the microcirculatory high-velocity blood flow. Specifically, an initial threshold, i.e. 1000 ?m/s, for the microcirculatory high-velocity blood flow of sepsis is proposed, which facilitates early diagnosis on sepsis. The changing process of the high-velocity blood flow shows development of the early-stage, intermediate-stage and end-stage sepsis.

A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.