A system and method for automated contrast arrival detection in temporally phased images or datasets of tissues effectively determines contrast arrival in regions that are substantially free of arteries. A plurality of tissue voxels in a plurality of temporally phased images are identified as a function of voxel enhancement characteristics associated with discrete tissue voxels. A processor/process computes average enhancement characteristics from the plurality of identified tissue voxels. The average enhancement characteristics are compared with predetermined average enhancement characteristics associated with contrast media arrival phases. Contrast media arrival phases in the temporally phased images are provided based on the comparison.

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image, a probability logic that computes a probability that the nodule is benign or that the nodule will respond to a treatment, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.

A method for performing flow analysis in a target volume of a moving organ having a long axis, such as the heart, from 4D MR Flow volumetric image data set of such organ, wherein such data set comprises structural information and three-directional velocity information of the target volume over time, the devices, program products and methods comprising, under control of one or more computer systems configured with specific executable instructions: a) deriving from the 4D MR Flow volumetric image data set at least one derived image data set related to the long axis of the moving organ, for example, by using a multi planar reconstruction: b) determining at least one feature of interest in the 4D MR Flow volumetric image data set or in said derived image data set. The feature of interest may be determined, for example, by receiving input from a user or by performing automatic detection steps on the 4D MR Flow volumetric image data set; c) tracking the feature of interest within the 4D MR Flow volumetric image data set or in the derived image data set; d) determining the spatial orientation over time of a plane containing the feature of interest in the 4D MR Flow volumetric image data set; c) performing quantitative flow analysis using velocity information on the plane as determined in step d). A corresponding device and computer program are also disclosed.

A computer aided diagnostic system and automated method to classify a kidney. Image data for a medical scan that includes image data of a kidney may be received. The kidney image data may be segmented from other image data of the medical scan. One or more iso-contours may be registered for the kidney image data, and renal cortex image data may be segmented from the kidney image data based on the one or more registered iso-contours. The kidney may be classified by analyzing one or more features determined from the segmented renal cortex image data using a learned model associated with the one or more features.

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image, a probability logic that computes a probability that the nodule is benign or that the nodule will respond to a treatment, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

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 data processing method for determining an enhancing structure of interest within an anatomical body part, wherein the structure of interest exhibits an enhanced signal in an image of the anatomical body part generated by a medical imaging method using a contrast agent, said method being designed to be performed by a computer and comprising a region growing algorithm.

A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.

The present invention provides methods to predict the treatment response of brain tumors such as glioblastoma multiforme to anti-angiogenic therapy based on quantitative perfusion-weighted MRI that can optionally be combined with intra-tumor specific molecular profiling. Since only a subset of brain cancer patients will benefit from anti-angiogenic therapy, identification of this subset is critical so that the effectiveness of the patient's current anti-cancer treatment regimen and the patient's survival likelihood can be increased by the inclusion of an anti-angiogenic agent.