Systems and methods for generating three-dimensional fluid flow simulations from two-dimensional (2D) image data are provided. Data is segmented from 2D images of a sample having a biological structure with fluid flow therethrough. Three-dimensional (3D) geometries are generated from the segmented data, and then a 3D reconstruction of the biological structure is generated from the 3D geometries. This 3D geometric computational analysis tool can be used to evaluate fluid dynamics and hemodynamics in the context of the structure anatomy and geometry.

In order to assess coronary artery disease from medical image data, an anatomical model of a coronary artery is generated from the medical image data. A velocity of blood in the coronary artery is estimated based on a spatio-temporal representation of contrast agent propagation in the medical image data. Blood flow is simulated in the anatomical model of the coronary artery using a computational fluid dynamics (CFD) simulation using the estimated velocity of the blood in the coronary artery as a boundary condition.

A method for determining the velocity of a fluid in a volume to be imaged of an examination object with the aid of an imaging method is described. Attenuation values are acquired based upon image data of the volume to be imaged, depending on location and time. A temporally and spatially delineated region is specified based upon the acquired attenuation data, in which the acquired attenuation data behaves approximately linearly. Subsequently, temporal and/or spatial gradients and/or a combination of a temporal and a spatial gradient are determined based upon the attenuation values associated with the temporally and spatially delineated region. Finally, the velocity of the fluid is calculated based upon the determined temporal and/or spatial gradients or from the combination of a temporal and a spatial gradient and from the temporal gradient. A fluid velocity determining device, non-transitory computer readable medium and a computed tomography system are also described.

A three-dimensional image of a patient is generated in time; blood vessels site voxels are compared to a model arterial and venous signals; clusters of voxels are separated so that ones that have spatial growth over time are determined to be bleeding sites.

A method for computing image-derived biomarkers includes receiving image data defining a three-dimensional image volume representative of an anatomical region of interest. Features characterizing local variations of intensity in the image data using an intensity model are identified. The features are used to perform one or more modeling computations directly on the image data to derive information related to a biomarker of interest.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The invention relates to a device for processing CT imaging data, comprising a processing unit, which is configured to receive a plurality of sets of CT imaging data recorded at different imaging positions and at different points in time. Furthermore, the processing device is configured to provide a plurality of auxiliary sets of CT imaging data, each auxiliary set of CT imaging data comprising processed image data allocated to spatial positions inside a respective spatial section of the object space, wherein a given one of the spatial sections contains those spatial positions which are covered by those sets of CT imaging data acquired at a respective one of the imaging positions, and to generate the processed image data for a given spatial position using those of the sets of CT imaging data acquired at the respective one of the imaging positions.

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.

Systems and methods are provided for assessing patient blood flow using video image processing. According to one aspect, a method of analyzing at least one blood flow characteristic of a patient includes capturing a video including a plurality of frames of an arterio-venous (AV) fistula on the patient; amplifying motion in the video to produce a motion-amplified video; determining a difference in intensity between consecutive frames in the motion-amplified video to produce a time-function of an amplitude of the optic flow representing movement in an area of interest on the patient; and determining the at least one blood flow characteristic of the patient based on the time-function.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.