The present disclosure provides a method for correcting a resting blood flow velocity on the basis of an interval time between angiogram images, comprising: acquiring, in an angiography state, an average blood flow velocity V.sub.h from a coronary artery inlet to a distal end of a coronary artery stenosis (S100); acquiring a time difference Δt between start times of two adjacent bolus injections of contrast agent (S200); obtaining a correction coefficient K according to the time difference Δt (S300); obtaining a resting blood flow velocity V.sub.j according to the correction coefficient K and the average blood flow velocity V.sub.h (S400), as well as an apparatus configured for implementing the above method. The disclosure obtains the resting blood flow velocity V.sub.j according to the correction coefficient K and the average blood flow velocity V.sub.h.

An endoscope system has an oxygen saturation calculation unit that calculates the oxygen saturation of an observation object, and includes an image acquisition unit that acquires a preliminarily captured image that is an image of the observation object, a correction unit that corrects an LUT that the oxygen saturation calculation unit uses for the calculation of the oxygen saturation, using the preliminarily captured image, a determination unit that determines a success or failure of the correction, and a warning unit that performs warning in a case where a determination result obtained by the determination unit is a failure.

A first acquisition unit acquires stent regions from each of three-dimensional images. A second acquisition unit acquires blood vessel regions from each of the three-dimensional images. A positioning unit acquires a first positioning result by positioning the blood vessel regions for each of the three-dimensional images. A deviation information acquisition unit acquires deviation information indicating a deviation of a stent from a blood vessel between the three-dimensional images based on the stent regions for the three-dimensional images and a deformation vector which is the first positioning result.

A registration facility and a registration method are provided where a pre-interventionally generated simulation model of an examination object is registered with an intra-interventional live image. The simulation model is adapted to the live image using at least one simulated course line of an anatomical feature and/or an instrument by minimizing a line distance metric, specified as a cost function, for a distance between the simulated course line and an actual intra-interventional course of the instrument that is visible in the live image.

A method and a product for AI recognizing of embolism based on VRDS 4D medical image, the method is applied to a medical imaging apparatus, and the method includes the following steps: determining a bitmap (BMP) data source according to a plurality of scanned images of a target site of a target user, wherein the target site includes an embolism formed on a wall of a target blood vessel; generating target medical image data according to the BMP data source; performing 4D medical imaging according to the target medical image data and determining a feature attribute of the embolism according to an imaging result, wherein the feature attribute includes at least one of the following: density, crawling direction, correspondence with a site of cancer focus and edge characteristics; and determining a type of the embolism according to the features and outputting the type.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

A method is for the characterization of plaque in a region of interest inside an examination subject by way of a plurality of image data sets. The image data sets have been reconstructed from a plurality of projection data sets, which have been acquired via a CT device using different X-ray energy spectra. The method includes: acquiring the image data sets, which include a plurality of pixels. Spectral parameter values are acquired on a pixel by pixel basis using at least two image data sets. Character parameter values are then acquired on a pixel by pixel basis to characterize plaques on the basis of the spectral parameter values. An analysis unit and a computed tomography system are also disclosed.

In some embodiments, an apparatus for imaging blood within a target region of tissue includes an imaging device configured to output image data associated with light received by the imaging device having a first and second spectral ranges, wherein the absorptivity by blood of light having the first spectral range is less than the absorptivity by blood of light having the second spectral range, and a controlling element configured to capture the image data associated with light received by the imaging device and to process the captured image data associated with light having the first spectral range and the captured image data associated with light having the second spectral range to generate compound image data associated with an amount of blood within the target region of tissue.

The present invention relates to a method and system for augmenting aneurysm learning data for augmenting artificial images formed of various result values calculated from simulation results. The method of augmenting aneurysm learning data according to the present invention includes: performing a simulation using aneurysm data; predicting a position having a smallest thickness in an aneurysm based on a result of the simulation; setting a center position at the predicted position; setting a plurality of peripheral positions at different positions having a preset radius from the center position; extracting blood flow data according to a preset sampling period for a reference time at each of the center position and the plurality of peripheral positions; converting the extracted blood flow data into an image to generate a blood flow image; and generating a central image and a peripheral image in which a plurality of blood flow images according to the center position and the peripheral position are arranged in the order of the reference time; and generating different artificial images by changing an arrangement order of the central image and the peripheral image.

A system and method for performing speckle correlation flow cytometry (SCFC). By subtracting out the stationary background when shining light through a sample (e.g., a vessel within a biological tissue), light only scattered by the desired targets (e.g., cells) can be captured and different types of targets (e.g., cells) can be distinguished by the autocorrelation of the speckle pattern. In this way, the targets (e.g., cells) can be classified and counted based on the features of their speckle correlations. The technique can be applied not only for noninvasive, label-free, in vivo CTC counting but also for counting other types of blood cells such as white blood cells or red blood cells.