An exemplary system, method, and computer-accessible medium for generating an image(s) of an three-dimensional anatomical flow map(s) can include receiving an optical coherence tomography (“OCT”) signal(s), splitting the OCT signal(s) into a plurality of subspectra, averaging the plurality of subspectra, and generating the image(s) of the three-dimensional anatomical flow map(s) based on the averaged subspectra. The OCT signal(s) can be a swept-source OCT signal. The OCT signal(s) can be split into the subspectra based on a Hamming window. The Hamming distance window can be optimized to minimize a nearest side lobe for each of the subspectra. A position of at least one of the subspectra can be shifted prior to averaging the subspectra. The position of all but one of the subspectra can be shifted prior to averaging the subspectra.

The present invention provides a rapid calculation method and system for a plaque stability index based on a medical image sequence. The system includes an image acquisition module, an image receiving module, an image processing module, a finite element calculation module and a result visualization module. The image acquisition module and the image receiving module are configured to acquire, receive and transmit a dynamic two-dimensional vascular image sequence; the image processing module is configured to acquire a space-transformational displacement field function by taking a local feature or a global image as a registration based on dynamic information of real-time deformation of an artery under a two-dimensional image; and the finite element calculation module is configured to acquire a time-dependent vascular lumen diameter sequence and a contour deformation parameter as well as a mechanical index by calculation performed by virtue of the above-mentioned displacement field function.

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 control device according to an embodiment of the present technology includes an acquisition section, a block control section, and a calculator. The acquisition section acquires an image signal of a tissue of a living body irradiated with laser light and on which image-capturing has been performed. The block control section controls a size of a pixel block according to an image-capturing condition for the image-capturing on the tissue of a living body. The calculator calculates speckle data based on the acquired image signal, using the pixel block of which the size is controlled.

A non-invasive computer-based method and system for assessing a coronary stenosis or other blockage in an artery or other vasculature includes creating a computational model of the vasculature of interest, modeling blood flow through the vasculature, and determining the mean residence time through a given coronary artery segment, which is a direct assessment of physiological changes on the flow of blood as a result of the stenosis. In some embodiments, blood is modeled as a multi-phase fluid.

A method for calculating coronary artery fractional flow reserve includes determining myocardial volume by extracting myocardial images; locating a coronary artery inlet and accurately segmenting coronary arteries; generating a grid model required for calculation by edge detection of coronary artery volume data; determining myocardial blood flow in a rest state and CFR by non-invasive measurement; calculating the total flow at the coronary artery inlet in a maximum hyperemia state; determining the flow in different blood vessels in the coronary artery tree in the maximum hyperemia state and then determining flow velocity V.sub.1 in the maximum hyperemia state; using V.sub.1 as the flow velocity at the coronary artery inlet and calculating a pressure drop ΔP from the coronary artery inlet to a distal end of a coronary stenosis, and a mean intracoronary pressure Pd at the distal end of the stenosis P.sub.d=P.sub.a−ΔP, and calculating fractional flow reserve.

A method is disclosed for providing a virtual tomographic stroke follow-up examination image. In an embodiment, the method includes: receiving a sequence of temporally successive tomographic perfusion imaging data sets of a region for examination; calculating the virtual tomographic stroke follow-up examination image of the region for examination by applying a trained machine learning algorithm to the sequence of temporally successive tomographic perfusion imaging data sets received; and providing the virtual tomographic stroke follow-up examination image calculated.

An ultrasound diagnostic apparatus (1) includes a gate setting unit (11) that sets a Doppler gate in a blood vessel region by performing an image analysis on a B-mode image in which at least the blood vessel region is imaged; a Doppler processing unit (6) that generates a Doppler waveform image on the basis of Doppler data in the Doppler gate; a display unit (8) that displays the B-mode image and the Doppler waveform image; and an image enlargement unit (9) that, in a case where both the B-mode image and the Doppler waveform image are frozen by a user, displays an enlarged B-mode image in which the blood vessel region including the Doppler gate is enlarged, on the display unit (8).

An image acquisition unit acquires a CT image and one or more MRI images of the brain of a subject that has developed a cerebral infarction. An infarction region extraction unit extracts an infarction region corresponding to the time elapsed since the development from the MRI image. A registration unit performs registration between the CT image and the MRI image. An infarction region specification unit specifies the infarction region corresponding to the time elapsed since the development in the CT image on the basis of the result of the registration. A learning unit learns a discriminator which discriminates an infarction region corresponding to the time elapsed since the development in the CT image to be discriminated, using the infarction region corresponding to the time elapsed since the development, which has been specified in the CT image, as teacher data.

Systems and methods are provided for enhanced heart medical imaging operations, particularly as by incorporating use of artificial intelligence (AI) based fetal heart functional analysis and/or real-time and automatic ejection fraction (EF) measurement and analysis.