A blood flow volume quantification system based on a blood flow modeling includes: an image receiver configured to receive 4D flow magnetic resonance imaging (MRI) images and MRI images of a plurality of non-diseased individuals; a representative model generation unit configured to perform vessel segmentation based on the received MRI images and to designate a representative model from among the MRI images in which vessel segmentation has been performed, based on a predetermined criterion; an integrated model generation unit configured to register MRI images of other non-diseased individuals to the representative model to generate an integrated model; and a standard blood flow model generation unit configured to register the 4D flow MRI images to the integrated model to generate a standard blood flow model.

Methods for characterizing fluids from a patient are provided. Characterizing the fluids includes estimating a volume of blood within fluids being drawn through a conduit with a system including a camera, one or more processors, and a display. A time series of images of the conduit are received, a concentration of a blood component of the fluids in the conduit is estimated based on the images, and a volumetric flow rate of the fluids passing through the conduit is estimated by tracking one or more pixels throughout at least a portion of the time series of images. Further, a volumetric flow rate of blood passing through the conduit is estimated based on the estimated concentration of the blood component and the estimated volumetric flow rate of the fluids. Finaly, an alert is shown on the display in response to the volumetric flow rate of blood being above a threshold.

A system includes a processor circuit that receives intravascular imaging data from an intravascular imaging catheter, an x-ray image from an x-ray imaging device, and intravascular pressure data from an intravascular pressure-sensing guidewire. The processor circuit correlates the intravascular imaging data and the intravascular pressure data to locations along a body lumen shown in the x-ray image. The processor circuit generates a longitudinal view of the body lumen based on the intravascular imaging data and outputs a screen display including the longitudinal view of the body lumen with an overlaid graphical representation corresponding to the intravascular pressure measurements.

An analysis method and an electronic device for a coronary artery image are provided. The method includes: performing segmentation on the coronary artery image based on a machine learning model to obtain a plurality of categories; setting one of the categories as a currently evaluated vessel, and determining whether a pixel quantity corresponding to the currently evaluated vessel is less than a first threshold to generate a result; and determining whether the coronary artery image has an occlusion phenomenon according to the result.

The present disclosure provides a non-transitory computer-readable recording medium storing a radiographic image analysis program that causes a computer to execute: acquiring a plurality of frame images generated by performing dynamic imaging by irradiating a subject with radiation under an imaging condition corresponding to a combination of a plurality of types of dynamic analyses set in advance; and executing, based on the plurality of frame images, the plurality of types of dynamic analyses included in the combination.

A method for quantitative measurement of cerebral vascular reactivity (CVR) combines sequential gas delivery with R.sub.2*-based perfusion analysis. Sequential gas delivery imposes a first stepwise reoxygenation after a first hypoxic condition and a second stepwise reoxygenation after a second hypoxic condition. In one mode, the second hypoxic condition produces greater vasodilation than the first; in another mode both hypoxia levels are minimal and an independent vasoactive stimulus, such as hypercapnia or acetazolamide, is applied between reoxygenations. MRI gradient-echo imaging records the R2* time course in a target voxel during each reoxygenation. Sigmoid fitting yields perfusion metrics including relative cerebral blood flow, relative cerebral blood volume and mean transit time. Comparison of the metrics derived from the two reoxygenations provides a numerical CVR value that can be reproduced across sessions and subjects.

The present disclosure provides a method for ultrasound image processing. at least one ultrasound image acquired by an ultrasound scan may be obtained. each ultrasound image is associated with a blood flow velocity. For each of the at least one ultrasound image, an envelope curve may be determined based on the ultrasound image; a plurality of first maximum points of the envelope curve may be determined; a plurality of second maximum points by screening the plurality of first maximum points may be obtained based on amplitude features of the plurality of first maximum points; and one or more parameters relating to the corresponding blood flow velocity may be determined based on the plurality of second maximum points.

The invention generally provides systems and methods for performing vessel segmentation from flow data, such as but not limited to 4D flow Magnetic Resonance Imaging (MRI) data. In certain aspects, the systems and methods of the invention may involve receiving flow data representative of flow in a vessel (such as 4D MRI flow data); identifying net flow effects in the flow data (such as 4D MRI flow data) according to a standardized difference of means (SDM) velocity that involves quantifying a ratio between net flow and observed flow pulsatility in each voxel of the received flow data (such as 4D MRI flow data); and identifying voxels with higher SDM velocity values than stationary tissue voxels, thereby performing vessel segmentation from flow data (such as 4D MRI flow data).

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 actual measurement value calculation unit calculates a first actual measurement value of oxygen saturation of a tissue to be observed. A reference value calculation unit calculates a first reference value of the oxygen saturation of the tissue to be observed. A relative value calculation unit calculates a relative value of the first actual measurement value with reference to the first reference value. An image generation unit generates an image of the relative value of the first actual measurement value on the basis of an evaluation color table to generate an evaluation oxygen-saturation image. A display unit displays the evaluation oxygen-saturation image.