According to one embodiment, a medical information processing method generates a high-contrast image by applying conversion processing to a first medical image captured by a first diagnostic apparatus and having a first contrast in a region of interest, the high-contrast image having a contrast higher than a contrast of a second medical image obtained by a second diagnostic apparatus. The method generates a pseudo second medical image by applying image processing to the high-contrast image, the pseudo second medical image simulating the second medical image and having a second contrast lower than the first contrast. The method trains a model using the pseudo second medical image as input data and the high-contrast image as ground truth data, and generates a trained model.

Systems, methods and computer-readable storage mediums relate to generating an image that includes functional, anatomical, and physiological images. The generated image may be an integrated image based on the functional image on which the anatomical and physiological images are mapped. The generated image may indicate more than one location of optimal lead placement. The generated image may be useful in pre-planning cardiac intervention procedures.

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 of modeling that allows for kinetic parameters to be extracted from DCE-MRIs performed with the temporal resolutions commonly used in the clinical setting is described herein. The kinetic parameters can be combined with analytic models of cancers to create predictors of disease recurrence.

A method for automated classification of curve patterns associated with dynamic image data of a lesion in a subject in order to determine characteristics of the lesion. The method comprising the steps of loading the image data into an electronic memory means, producing a plot of signal intensity profile, converting the signal intensity profile into a contrast enhancement profile, detecting a reference enhancement profile having a highly positive slope over an initial enhancement period followed by a decreasing profile portion, deriving signature curve types based on the reference enhancement profile, classifying an enhancement curve for each pixel in a selected ROI into one of the derived signature curve types using all available time points and displaying a grid-plot of the classified enhancement curves for all pixels in the selected ROI, wherein the overall display of curves and heterogeneity provides visual indication of the characteristics of the lesion.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain one or more complex product signal values each indicating a signal value of a complex product and a complex ratio signal value indicating a signal value of a complex ratio calculated in units of pixels by using first data and second data successively acquired by implementing a gradient echo method after an Inversion Recovery (IR) pulse is applied and to derive a T1 value of each of the pixels from one of the complex product signal values selected on the basis of the obtained complex ratio signal value.

The method comprises identifying, in a 3D volume, a zone of a first type (H), a zone of a second type (BZ) and a zone of a third type (C) and:automatically identifying as a candidate channel (bz) a path running through the zone of a second type (BZ) and extending between two points of the zone of a first type (H); andautomatically performing, on a topological space (H_and_BZ_topo), homotopic operations between the candidate channel (bz) and paths (h) running only through the zone of a first type (H), and if the result of said homotopic operations is that the candidate channel (bz) is not homotopic to any path running only through the zone of a first type (H) identifying the candidate channel (bz) as a constrained channel. The computer program product implements the steps of the method of the invention.

Disclosed are methods and systems for calculating a contrast reagent (CR) extravasation rate constant and generating a contrast reagent leakage corrected relative cerebral blood volume (rCBV) image map of a brain region from dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) time-course image data based on pharmacokinetic first principles. In one example approach, a computerized method may include performing a linearization transform of a DSC MRI time-course equation which accounts for an intravascular contribution and an extravasating component, and calculating CR leakage from a slope of a linear portion of the transformed data.

The method comprising acquiring and analyzing by computer means image information of video sequences of two or more dimensions obtained from contrast-enhanced signals, for example ultrasound, coherence tomography, fluorescence images, or Magnetic Resonance Imaging, of a body part or tissue, for example of an organ, of a living subject; detecting events from said information of video sequences; selecting a Region of Interest of said body part or tissue; computing a first graph representative of a local vascular network of said image information of video sequences in which the edges of the graph are estimated by the temporal relationship among spatially remote signals of said image information of video sequences within a set of video sequences; and using said graph for assessment of vascular networks.

A computing device determines a contrast medium uptake time using magnetic resonance imaging data. Image data constructed from data generated by a magnetic resonance imaging (MRI) machine of a subject is read. A representation computed from the read image data is presented on a display device. Baseline artery locations identified within the presented representation that are associated with a baseline artery are received. A first time-of-arrival (TOA) of contrast medium into the baseline artery is determined using the received baseline artery locations and the read image data. For a plurality of locations within the read image data excluding the baseline artery locations, a second TOA of the contrast medium into a respective location relative to the determined first TOA is determined using the read image data, and the determined second TOA is stored in association with the respective location to assist in lesion identification for the subject.