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.

A method includes fusing at least three images together into a single fused image, wherein at least one of the three images includes a binary-pattern representation image. A system includes an image processing system (100) that combines an anatomical image, a functional image and a binary-pattern representation image into a single image. A computer readable storage medium encoded with computer executable instructions, which, when executed by a processor of a computer, cause the processor to combine an anatomical image, a functional image, and a binary-pattern representation of a different functional image into a single image such that the anatomical image and the functional image are visible in interspaces between binary points of the binary-pattern representation of the functional image.

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.

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.

A method of determining a residue function in brain tissue, from medical images acquired after introducing contrast agent into the blood, correcting for contrast agent leakage into the tissue, comprising: a) providing time signals indicating contrast agent concentration for leaking voxels, a time signal indicating average contrast agent concentration for non-leaking voxels, and an artery input function, all derived from the images; b) fitting the leaking voxel signals to a model time signal with a free parameter for leakage rate, the model assuming that the concentration of contrast agent perfusing through a leaking voxel has a same shape as a function of time as the average contrast agent concentration for non-leaking voxels; c) using the best fit leakage rate parameter to make a correction for leakage to the leaking voxel signals; and d) deconvolving the corrected signals from the artery input function, to find the residue function.

Methods, apparatus, and other embodiments distinguish disease phenotypes and mutational status using co-occurrence of local anisotropic gradient orientations (CoLIAGe) and Laws features. One example apparatus includes a set of circuits that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating breast cancer, computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, extracts a set of texture features from the MRI image, and classifies the phenotype of the breast cancer based on the feature vector and the set of texture features. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image. Example methods and apparatus may operate substantially in real-time, or may operate in two, three, or more dimensions.

A dynamic analysis apparatus includes: an obtainment unit configured to set a region of interest in dynamic images obtained by photographing a dynamic state by irradiation of a check target part with radial rays, and obtain movement information on movement of the region of interest; a determination unit configured to determine an emphasis level of a pixel signal value of an attentional pixel corresponding to a pixel in the region of interest on the basis of the movement information of the region of interest obtained by the obtainment unit; and a correction unit configured to correct the pixel signal value of the attentional pixel of the dynamic images or analysis result images generated by analyzing the dynamic images, on the basis of the emphasis level determined by the determination unit.

A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium (Gd) enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging.

The invention provides for a medical imaging system (100, 300, 500) comprising a processor (104). Machine executable instructions cause the processor to: receive (200) magnetic resonance data (120) comprising discrete data portions (612) that are rotated in k-space; bin (202) the discrete data portions into predetermined motion bins (122) using a motion signal value; reconstruct (204) a reference image (124) for each of the predetermined motion bins; construct (206) a motion transform (126) between the reference images; bin (208) a chosen group (610) of the discrete data portions into a chosen time bin (128). Generate an enhanced image (130) for the chosen time bin using the chosen group fo the discrete data portions and the motion transform of each of the chosen group to correct the discrete data portions.