The present invention provides a diagnostic imaging system (100), comprising a magnetic resonance (MR) imaging system (110) for providing an image representation of at least a portion of a subject of interest (120) positioned in an examination space (116), a hyperthermia device (111) for locally heating a target zone within the portion of the subject of interest (120), and a control unit (126) for controlling the MR imaging system (110) and the hyperthermia device (111), wherein the diagnostic imaging system (100) is adapted to provide a diagnostic image representation of the portion of the subject of interest (120) by correlating image representations obtained at different temperatures of the target zone, wherein the diagnostic image representation comprises information on temperature dependent changes of the metabolism of the subject of interest (120). The invention further provides a treatment system comprising the above diagnostic imaging system (100), a treatment module (146) for applying a treatment to the subject of interest (120) for destroying cells within the target zone, and a control module (126) for controlling the treatment module (146) for applying the treatment based on diagnostic image representations obtained by the diagnostic imaging system (100). Accordingly, changes of the metabolism of the subject of interest can be evaluated to direct a treatment to such areas, where the cells have not yet been destroyed as desired. By comparing the difference of the above measurements between the first and the second temperature, the efficiency of the treatment can be evaluated and the further treatment can be adapted based on the metabolism changes.

A method for determining quantitative parameters for dynamic contrast-enhanced MR data includes acquiring a set of contrast-enhanced MR data for a region of interest using a T1-weighted pulse sequence, generating at least one contrast concentration curve based on the set of contrast-enhanced MR data, accessing a comprehensive dictionary of contrast concentration curves and generating a grouped dictionary that has a plurality of groups based on the comprehensive dictionary. Each group includes a plurality of correlated contrast concentration curves and a group representative signal for the group. The method also includes comparing a contrast concentration curve with the group representative signal of each group to select a group, comparing the contrast concentration curve to the plurality of correlated contrast concentration curves in the selected group to identify a set of quantitative parameters for the concentration curve and generating a report including the set of quantitative parameter.

Embodiments access a radiological image of tissue having a tumoral volume and a peritumoral volume; define a vasculature associated with the tumoral volume; generate a Cartesian two-dimensional (2D) vessel network representation; compute a first set of localized Hough transforms based on the Cartesian 2D vessel network representation; generate a first aggregated set of peak orientations based on the first set of Hough transforms; generate a spherical 2D vessel network representation; compute a second set of localized Hough transforms based on the spherical 2D vessel network representation; generate a second aggregated set of peak orientations based on the second set of Hough transforms; generate a vascular network organization descriptor based on the aggregated peak orientations; compute a probability that the tissue is a member of a positive class based on the vascular network organization descriptor; classify the ROI based on the probability; and display the classification.

The present disclosure relates to a spinal stenosis diagnostic method and apparatus and the spinal stenosis diagnostic method includes: sequentially receiving a phase contrast magnetic resonance imaging in each time interval captured by normalizing one cardiac cycle with a plurality of time intervals; obtaining a cerebrospinal fluid velocity distribution in each normalized time interval from the phase contrast magnetic resonance imaging of each time interval; calculating a turbulence kinetic energy using the cerebrospinal fluid velocity distribution obtained at every time interval; and diagnosing the spinal stenosis using the calculation result of the turbulence kinetic energy.

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.

Embodiments access a pre-neoadjuvant chemotherapy (NAC) radiological image of a region of tissue demonstrating breast cancer (BCa), the region of tissue including a tumoral region, the image having a plurality of pixels; extract a set of patches from the tumoral region; provide the set of patches to a convolutional neural network (CNN) configured to discriminate tissue that will experience pathological complete response (pCR) post-NAC from tissue that will not; receive, from the CNN, a pixel-level localized patch probability of pCR; compute a distribution of predictions across analyzed patches based on the pixel-level localized patch probability; classify the region of tissue as a responder or non-responder based on the distribution of predictions, and display the classification. Embodiments may further generate a probability mask based on the pixel-level localized patch probability; and generate a heatmap of likelihood of response to NAC based on the probability mask and the pre-NAC radiological image.

In a method, computer and magnetic resonance (MR) apparatus for normalizing MR contrast images of an examination object that has two chemically different substances (SW, SF), wherein the first substance produces a first image signal and the second substance produces a second image signal, a processor is provided with a complex-valued contrast having pixels with signal contributions from the first and second substances. A phase correction of this contrast image is performed by calculating a real-valued contrast from the amount of the image signals of each pixel of the complex-valued contrast image. A mathematically smooth correction map is determined based on a number of the pixels that have a defined real-valued contrast. The intensity of pixels of the complex-valued contrast image are homogenized with other scans based on the correction map.

A method for detecting an indication of likelihood of malignancy, comprising: receiving a sequence of anatomical images of a breast of a target individual acquired over a time interval during which contrast is administered, analyzing the sequence of anatomical images to identify: a baseline pre-contrast image denoting lack of contrast, a peak contrast image denoting a peak contrast enhancement, an initial uptake image denoting initial contrast enhancement, and a delayed response image denoting final contrast enhancement, creating a multi-channel image representation comprising: intensity channel including the peak contrast enhanced image, contrast-update channel including the computed difference between the peak contrast enhanced image and the pre-contrast image, and contrast-washout channel including the computed difference between the initial uptake image and the delayed response image, and computing by a trained deep convolutional neural network, a classification category indicative of likelihood of malignancy for the sequence according to the multi-channel image representation.

A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.

A method is provided for flow analysis in a target volume of a moving organ, which involves a sequence of first volumetric image data sets that include structural information and three-directional velocity information of the target volume and a sequence of second volumetric image data sets that include structural information of the target volume. The method involves tracking a feature of interest within the sequence of the second volumetric data sets, determining time varying spatial orientation of a plane containing the feature of interest in the sequence of the first volumetric image data sets by transferring the plane from the second volumetric image data sets to the first volumetric image data sets, reformatting the three-directional velocity information into one-directional velocity information on the plane, and performing bi-dimensional quantitative flow analysis using the one-directional velocity information. A corresponding apparatus and computer program are also disclosed and claimed.