Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image segmentation algorithm (122). The image segmentation algorithm is configured for outputting one or more prede-termined anatomical regions within initial magnetic resonance imaging data (124) descriptive of a predetermined field of view (109) of a subject (318). The medical system further comprises a computational system (104), wherein execution of the machine executable in-structions causes the computational system to: receive (200) the initial magnetic resonance imaging data (124); receive (202) the image segmentation comprising the one or more anatomical regions within the magnetic resonance imaging data in response to inputting the initial magnetic resonance imaging data into the image segmentation algorithm; select (204) at least one of the one or more anatomical regions as a selected image portion (128) using a predetermined criterion; and reduce (206) image intensity within the selected image.

A system, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics obtains measurement information associated with a parameter of a voxel of tissue of a patient measured at two or more time points, the two or more time points occurring before one or more characteristics of the voxel of the tissue are separable in an image generated based on the parameter of the voxel measured at a single time point of the two or more time points, and determines, based on the parameter of the voxel at the two or more time points, the one or more characteristics of the voxel of the tissue.

A method and a system for measuring intra-site heterogeneity in a tumor using magnetic resonance imaging (MRI). The method includes acquiring magnetic resonance (MR) images using MRI modality; segmenting tumor sites in the MR images; dividing each of the tumor sites into a plurality of sub-regions; deriving image biomarkers from each voxel or pixel in the plurality of sub-regions; classifying each voxel or pixel in the plurality of sub-regions into genotypes or molecular subtypes based on the extracted image biomarkers and a classifier model including associations between image biomarkers and genotypes or molecule subtypes; creating a distribution of genotypes or molecular subtypes in the each of the plurality of sub-regions based on classifications of voxels or pixels; generating spatial information of genotypes or molecular subtypes in the tumor sites based on the distribution; and measuring intra-site heterogeneity in the tumor sites.

DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

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.

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.

A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (200); training the CNN (200) to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN (200) to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.

A system and method of imaging tissue includes administering a contrast agent having charged gold nanoparticles in suspension into a vessel of the subject, such that the nanoparticles are carried into the tissue; and performing microwave imaging of the tissue after administering the contrast agent. In embodiments, the nanoparticles have a tissue-selective protein tag. In embodiments, images are taken prior to administering the contrast agent, and further images may be taken during an agent—washout period after imaging with contrast agent. The contrast agent is injectable, with the nanoparticles suspended as a colloid in a biocompatible, isotonic, carrier. In particular embodiments, the nanoparticles have median diameter of less than fifty nanometers, or less than five nanometers, and may have a tissue-selective protein tag. A microwave imaging system has injection apparatus with the gold-nanoparticle agent, and is configured to take, and difference, pre and post contrast images as well as washout images.

A method for dynamic contrast enhanced (DCE) image processing and kinetic modeling of an organ's region-of-interest is provided. The method includes deriving at least a contour of an exterior of the organ's region-of-interest from one or more of a plurality of images; generating a spline function in response to the derived contour of the exterior of the organ's region-of-interest from the one or more of the plurality of images; registering the plurality of images wherein the organ's region-of-interest has been segmented; deriving a tracer curve for the organ's region-of-interest in the registered images, the tracer curve indicating a change in concentration of a contrast agent flowing through the organ's region-of-interest over a time period; and kinetic modeling by fitting a kinetic model to the tracer curve to generate one or more maps of tissue physiological parameters associated with the kinetic model.

The described invention provides systems and methods for detecting and segmenting a lesion from longitudinal, time series, or multi-parametric imaging by utilizing spectral embedding-based active contour (SEAC). In addition, the described invention further provides systems and methods for registering time series data by utilizing reduced-dimension eigenvectors derived from spectral embedding (SE) of feature scenes (SERg).