This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.

An adaptive template image for registering a PET or a SPECT image includes a template image model including variability of values for each voxel in a template image according to one or more control parameters.

Medical imaging methods for processing a three-dimensional (3D) image data set with two-dimensional X-ray images from an X-ray machine using a target function. Methods can include providing a 3D image data set of at least one examination zone in which anatomical structures are present, segmenting the image data set to provide a 3D vascular structure model and a 3D bone structure model, recording a first two-dimensional (2D) X-ray image containing at least a portion of the vascular structure and at least a portion of the bone structure, recording a second 2D X-ray image of the examination zone at a different contrast agent concentration, and subtracting the first and second 2D X-ray images to generate a subtraction image. An optimum projective geometry may then be determined using a three-part target function based on the 3D image data and the 2D X-ray images.

A method is for computed tomography imaging. In an embodiment, the method includes provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method; suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; and analyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type. A corresponding device, a control device for a computed tomography system or a diagnosis system, and a diagnosis system and a computed tomography system are also disclosed.

A radiographic image capturing system is described. If a hardware processor determines that an image processor and an image analyzing unit are capable of sharing image data via an identical memory, the image processor stores image data in the memory and the image analyzing unit analyzes the image data with reference to the memory. If the hardware processor determines that the image processor and the image analyzing unit can send and receive the data via a wired network, the image processor transfers the data to the image analyzing unit, and the image analyzing unit analyzes the data. If the hardware processor determines that the image processor and the image analyzing unit can send and receive the data via a wireless network, the image processor compresses the data or decimates partial data and transfers the data to the image analyzing unit, and the image analyzing unit decompresses and analyzes the data.

A medical information processing apparatus according to an embodiment includes a memory storing a model that represents a relation between cell deformation and electric signal propagation; and processing circuitry configured to acquire four-dimensional image data acquired by imaging a three-dimensional structure of a cardiac muscle in time series, extract movement information on the cardiac muscle at multiple positions in the four-dimensional image data, and apply the model to the movement information with respect to the positions to calculate electric signal propagation at the positions.

Images can be generated with overlays indicating an amount of brain tissue damage based on the disruption of blood supply. Imaging data can be analyzed to determine perfusion parameters with respect to regions of the brain of an individual. The thresholds for the perfusion parameters with respect to the presence of damaged brain tissue can be based on a period of time elapsed since the onset of a biological condition disrupting blood flow to one or more regions of the brain of the individual. The imaging data can also be analyzed to determine measures of hypodensity with respect to regions of the brain of the individual. A likelihood of the measures of hypodensity corresponding to damaged brain tissue can also be determined based on the period of time elapsed since the onset of the biological condition.

The present invention relates to a method of generating one or more skeletal characteristic values of a subject from a planar bone image, comprising identifying one or more regions of interest (ROIs) in the planar bone image and performing one or more feature analyses on the regions of interest to generate the skeletal characteristic values. The generated characteristic values correspond to the bone quality of the subject, and may be used to evaluate the overall bone status and future fracture risk.

A method and apparatus for analyzing diagnostic image data are provided in which a plurality of acquisition images of a vessel of interest having been acquired with a pre-defined acquisition method is received at a trained classifying device and classified, by the classifying device, to extract at least one quantitative feature of the vessel of interest from at least one acquisition image of the plurality of acquisition images. The at least one quantitative feature is then output associated with the at least one acquisition image while the acquisition of the diagnostic image data is still in progress and one or more adjustable image acquisition settings are adjusted based on the at least one quantitative feature to optimize the acquisition of the diagnostic image data.

A method of detecting a protrusion in an intestinal tract in a computer according to an embodiment of the present disclosure includes acquiring a three-dimensional model of the intestinal tract scanned by a scanning device, the three-dimensional model comprising three-dimensional data of the intestinal tract; mapping, in the computer, the three-dimensional model to a two-dimensional plane in an area-preserving manner; and detecting an area of the protrusion in the two-dimensional plane. The method can replace traditional modes such as enteroscopy, and the protrusion in the intestinal tract is detected in a painless and low-cost mode.