From a plurality of medical images in time phases, a target site is extracted from at least one medical image, a reference point is set on each of a target-site side, and a periphery side of the target site which are on across from each other over an outline of the extracted target site, and movement information for the reference points is calculated.

From a plurality of medical images in time phases, a target site is extracted t least one medical image, a reference point is set on each of a target-site side, and a periphery side of the target site which are on across from each other over an outline of the extracted target site, and movement information for the reference points is calculated.

An image registration device includes: an image acquisition unit that acquires plural images captured in time series; a pixel value change acquisition unit that acquires a pixel value change at the same position of each of the images for plural positions of each image; a clustering unit that clusters the pixel value changes acquired for plural positions of each image into plural classes; a region division unit that divides each of the images into plural regions based on information of the class of each pixel of each image and a pixel value of each pixel of each image; and a registration processing unit that performs registration processing on each image based on information of plural regions of each image.

A method for tracking an object in an image sequence with at least a first image and a second image includes the steps of: identifying a plurality of objects in the first image and a plurality of objects in the second image; determining matching probability of an object in the second image with an object in the first image, based on relative positional relationships between the plurality of objects in the first image and relative positional relationships between the plurality of objects in the second image; and determining, based on the determined matching probability, a matching result relating to whether the object in the second image matches with the object in the first image. A match indicates that the object in the second image corresponds with or originates from the corresponding object in the first image.

Determining collateral information describing blood flow in collaterals of a blood vessel system in a target region of a patient from a four-dimensional vascular data set describing image values of temporal flow of a contrast medium and/or marked blood constituents as recorded by a medical imaging device is provided. A method includes segmenting the blood vessel system in the vascular data set and determining collaterals among the segmented blood vessels by a collateral classifier. For all collaterals determined, a diameter of the collateral is determined taking into account the segmentation, a filling parameter describing the filling of the collaterals, and a time parameter describing the time response relative to a reference point in the blood vessel system from a temporal course of the image values in a portion of the collaterals under consideration. The method includes determining the collateral information from the diameter, the filling parameter, and the time parameter.

A computer-implemented method for providing a scene with synthetic contrast includes receiving preoperative image data of an examination region containing a hollow organ, wherein the medical image data images a contrast agent flow in the hollow organ; receiving intraoperative image data of the examination region of the examination subject, wherein the intraoperative image data images a medical object at least partially disposed in the hollow organ, generating the scene with synthetic contrast by applying a trained function to input data, wherein the input data is based on the preoperative image data and the intraoperative image data, wherein the scene with synthetic contrast images a virtual contrast agent flow in the hollow organ taking into account the medical object disposed therein, wherein at least one parameter of the trained function is based on a comparison between a training scene and a comparison scene; and providing the scene with synthetic contrast.

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

A method in a computing device for quantitative surgical image registration includes: prior to a surgical procedure, obtaining, using a first imaging modality, a preoperative image of patient tissue and a plurality of preoperative measurements of a material property of the patient tissue. The preoperative measurements correspond to respective points in the preoperative image. The method includes storing the preoperative image and the preoperative measurements, and during the surgical procedure, using a second imaging modality, capturing an intraoperative image of the patient tissue and a second plurality of intraoperative measurements of the material property of the patient tissue. The intraoperative measurements correspond to respective points in the intraoperative image image. The method includes comparing the first and second pluralities of measurements to determine a transformation for registering the preoperative image and the intraoperative image; and storing the transformation in association with one of the intraoperative image and the preoperative image.

A system and method are provided for medical imaging that includes acquiring, during a common imaging acquisition process, rotational, x-ray volume image data and x-ray tomosynthesis image data from a subject. The method includes reconstructing a time-resolved three-dimensional (3D) image volume from the rotational, x-ray volume image data and producing a four-dimensional (4D) image series of the subject with resolved overlapping features by selectively combining the time-resolved 3D image volume and the x-ray tomosynthesis imaging data.

An embodiment in accordance with the present invention provides a method for non-invasively determining the functional severity of coronary artery stenosis. The method includes gathering patient-specific data related to concentration of a contrast agent within a coronary artery of a patient using a coronary computed tomography angiography scan (CCTA). The patient-specific data is used to calculate a patient-specific transluminal attenuation gradient for the coronary artery of the patient. The patient specific transluminal attenuation gradient is used to determine an estimate of a coronary flow velocity, pressure gradient, loss coefficient, coronary flow reserve, and/or fractional flow reserve for the patient. Coronary flow velocity, pressure gradient, loss coefficient, coronary flow reserve, and fractional flow reserve can then be used to estimate the functional severity of coronary artery stenosis.