AUTOMATED DETECTION OF RISK AREA USING QUANTITATIVE T1 MAPPING

摘要

FIELD: medicine.;SUBSTANCE: medical imaging system includes a data storage, complete with an ability to store the map of image prior to contrast, generated before contrast agent injection, and map of image after contrast generated after contrast area injecting. Each image map is mutually combined and has a different contrast, one or more processors, performed with a possibility to cluster the value of matching pairs of image maps voxels before and after contrast on a dispersion graph, where the values of the image map before contrast with the values of the image map after contrast are plotted to identify clusters corresponding to each of the many tissue types. Tissue types include a healthy myocardium, blood, a heart attack and a gray area. The gray area includes pre-infarction and heterogeneous infarction, assigning one of the tissue types to each pair of voxels, and a display device. The medical imaging method includes the steps of obtaining the first image, obtaining the second image with a contrast other than the contrast of the firstT1 image. The first T1 image and the second T1 image are obtained using the same gradient echo sequence with inversive recovery, the first and the second T1 images are mutually combined, pairs of the measured contrast value of the corresponding voxels from the first and the second T1 images are created, measured value pairs belonging to clusters is assigned, wherein each cluster represents one of the many tissue types, one of the many tissye types is assigned each voxel of the diagnostic image of the corresponding rendered area using the first and the second T1 images_, and the diagnostic image is displayed. To implement the method, a medical imaging system is used that contains one or more processors. The image processing system comprises a data storage, a classification module and a diagnostic image display device with each classification tissue type depicted with a different contrast and colour.;EFFECT: use of inventions allows to improve automated detection of risk areas due to accurate identification of different tissue types.;19 cl, 4 dwg