The present application relates to a method and system for analyzing blood flow conditions. The method includes: obtaining images at multiple time phases; constructing multiple vascular models corresponding to the multiple time phases; correlating the multiple vascular models; setting boundary conditions of the multiple vascular models respectively based on the result of correlation; and determining condition of blood vessel of the vascular models.

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.

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

An image processing apparatus includes an information obtaining unit configured to obtain three-dimensional polarization sensitive tomographic information and three-dimensional motion contrast information about a subject based on tomographic signals of lights having different polarizations, the lights being obtained by splitting a combined light obtained by combining a returned light from the subject illuminated with a measurement light with a reference light corresponding to the measurement light, an obtaining unit configured to obtain a lesion region of the subject using the three-dimensional polarization sensitive tomographic information, and an image generation unit configured to generate an image in which the lesion region is superimposed on a motion contrast image generated using the three-dimensional motion contrast information.

A method includes obtaining volumetric image data that includes a coronary vessel of a subject. The method further includes identifying the coronary vessel in the volumetric image data. The method further includes identifying a presence of a collateral flow for the identified coronary vessel. The method further includes determining a boundary condition of the collateral flow. The method further includes constructing a boundary condition parametric model that includes a term that represents the boundary condition of the collateral flow. The method further includes determining a fractional flow reserve index for the coronary vessel with the boundary condition parametric model.

There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustment. There is also provided a system for doing the same.

An ultrasound imaging device and a clutter filtering method using the same are disclosed. The clutter filtering method using the ultrasound imaging device according to one embodiment includes obtaining ultrasound data from a field-of-view (FOV) of an object, generating decomposition data including common scale information by performing rank matrix decomposition once on all of the obtained ultrasound data, estimating local characteristic information by reflecting spatial information on each pixel to the common scale information, and extracting a blood flow signal by performing filtering on each pixel based on the estimated local characteristic information.

According to one embodiment, an image processing device includes processing circuitry. The processing circuitry sequentially acquires image data of time-sequential DSA images of an object, and acquires a parameter value for each pixel based on temporal change of a pixel value of the each pixel corresponding to the same region of the object in the sequentially acquired image data of time-sequential DSA images. Further, the processing circuitry sequentially generates image data of parameter images in such a manner that identification information according to the parameter value is assigned to the each pixel corresponding to the same region of the object, each time image data of a DSA image of the latest time phase being acquired.

A method and a corresponding system for assessing a haemodynamic parameter for a vascular region of interest of a patient based on angiographic images are provided. After acquiring multiple angiographic images, a three dimensional (3D) representation of at least a first portion of the respective region of interest is performed, and geometric features are extracted from complete or partial views. Additional geometric features are extracted from partial incomplete views. A complete set of 3D geometric features for an anatomical structure, such as a vessel tree, is then generated by combining the extracted geometric features and estimating any missing geometric features. Using the complete set of 3D geometric features, a feature-based assessment of the haemodynamic parameter, such as a fractional flow reserve, is then performed.

Computer aided visualization and diagnosis by volume analysis of optical coherence tomography (OCT) angiographic data. In one embodiment, such analysis comprises acquiring an OCT dataset using a processor in conjunction with an imaging system; evaluating the dataset, with the processor, for flow information using amplitude or phase information; generating a matrix of voxel values, with the processor, representing flow occurring in vessels in the volume of tissue; performing volume rendering of these values, the volume rendering comprising deriving three dimensional position and vector information of the vessels with the processor; displaying the volume rendering information on a computer monitor; and assessing the vascularity, vascular density, and vascular flow parameters as derived from the volume rendered images.