A method of quantifying a 3D fractional moving blood volume (3D-FMBV) in a tissue volume of a subject using an ultrasound system, including acquiring images of the tissue volume from a power doppler scan of the tissue volume; applying image enhancement settings to the images; segmenting an organ, tissue or region thereof from the image data; determining geometric partitions of the segments based on distance from the transducer head of the ultrasound system; and computing a 3D-FMBV using a 3D-FMBV analysis algorithm from the partitions.

A system and method for visualizing blood flow are disclosed. The method generally includes obtaining a laser speckle contrast imaging (LSCI) image of blood flow; obtaining a white light image of a tissue, the white light image capturing an anatomical structure of a subject in a region associated with the LSCI image of the blood flow; spatially registering the LSCI image and the white light image with one another; overlaying the spatially registered LSCI and white light images; and generating display data which includes the spatially registered LSCI and white light images and that continuously depicts the blood flow overlaying the tissue.

The present invention discloses methods for automatically computing an arterial input function from one or more regions of interest, the method comprising: a. obtaining a plurality of dynamic image data sets comprising volumetric image data from the regions of interest over multiple scanning intervals; b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest; c. automatically estimating, using artificial intelligence, an arterial input function based on plurality of dynamic image data sets combined with one or more time activity curves (TAC) in the region(s) of interest in target organ(s); and d. computing a pre-trained predictive pharmacokinetic AI model arterial input function using time activity curve input associated with region(s) of interest of target organ(s).

There is provided a medical image processing apparatus which includes a first extraction unit configured to extract coronary arteries depicted in images of a plurality of time phases relating to the heart, and to extract at least one stenosed part depicted in each coronary artery; a calculation unit configured to calculate a pressure gradient of each of the extracted coronary arteries, based on tissue blood flow volumes of the coronary arteries; a second extraction unit configured to extract an ischemic region depicted in the images; and a specifying unit configured to specify a responsible blood vessel of the ischemic region by referring to a dominance map, in which each of the extracted coronary arteries and a dominance territory are associated, for the extracted ischemic region, and to specify a responsible stenosis, based on the pressure gradient corresponding to a stenosed part in the specified responsible blood vessel.

The present disclosure provides a method for ultrasound image processing. an ultrasound image may be obtained. The ultrasound image may be associated with the blood flow velocity. An envelope curve may be determined based on ultrasound image. A plurality of first maximum points of the envelope curve may be determined. A plurality of second maximum points may be obtained by screening the plurality of first maximum points based on amplitude features of the plurality of first maximum points. A plurality of third maximum points may be obtained by correcting the plurality of second maximum points according to time features of the plurality of second maximum points. One or more parameters relating to the blood flow velocity may be determined based on the plurality of third maximum points.

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.

A method includes identifying stenosed region within vessel in vascular image data and generating a revascularized model of the vessel based on the vascular image data with a lumen boundary in the stenosed region adjusted to have a same cross-sectional area as healthy sections of the vessel. The method includes determining a first pressure distribution for the revascularized model at hyperemic flow, determining a second pressure distribution for the vessel in the vascular image data, and calculating a subtracted pressure distribution by subtracting the second pressure distribution from the first pressure distribution. The method includes determining an asymptotic value for the subtracted pressure distribution and calculating a value by subtracting the asymptotic value from a pressure value obtained from the second pressure distribution at a location at a beginning of the stenosed region. The method includes normalizing the value to obtain a location independent FFR value for the stenosed region.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

The present disclosure provides a graphical user interface (GUI) arranged to convey information related to the IVUS images and stent expansion to a user. The GUIs can be generated to include a longitudinal depiction of the vessel and lumen borders as well as a deployed stent. A slider to navigate along the longitudinal axis of the vessel is provided where a stent expansion ratio is dynamically updated based on the slider location.

Disclosed are a PET parameter determination method and apparatus, and a device and a storage medium. Comprises: extracting a tracer identifier from the PET scanning data; performing image reconstruction on the PET scanning data, so as to obtain a PET image set; according to the PET image set, determining a sampling time activity curve corresponding to each pixel, and according to the tracer identifier and a pre-created correlation between a tracer identifier and a tissue compartmental model, determining a tissue compartmental model corresponding to the sampling time activity curve; on the basis of the tissue compartmental model, modifying an activity addition expression corresponding to the intensity of each pixel point corresponding to the tissue compartmental model, so as to update the activity addition expression; and according to the updated activity addition expression, determining the numerical value of at least one dynamic parameter corresponding to the PET image set.