Clustering multidimensional MR images to detect metabolic changes in different tissue classes

摘要

A tissue database was established by using multidimensional clusters of exact longitudinal (T$- 1$/) and transversal (T$-2$/) relaxation times and spin density, allowing the automatic segmentation and characterization of healthy and pathologic tissue. All parameters were simultaneously acquired employing a modified Multi-Echo pulse sequence. Initial clinical results showed a good differentiation between normal brain tissue and pathologic tissue like edema and meningioma. Inhomogeneous tumors such as high-grade glioblastoma were difficult to characterize automatically. The implementation of a diffusion-weighted modified Tanner-Stejskal pulse sequence allows the acquisition of the Apparent Diffusion Constant (ADC), which has been incorporated for the first time into a multidimensional information set as a new tissue-characterizing parameter. This parameter is sensitive to changes in the mobility of water in and between different cell compartments resulting from metabolic cell disorders like ischemic or edematous processes. To reproduce the known results of animal experiments, where as early as 30 min after an ischemic event the measurement of the ADC led to a diagnosis, diffusion-weighted imaging had to be implemented on a standard clinical scanner. The correction of unavoidable motion artifacts, which occur when applying diffusion- weighted spin echo sequences on standard clinical scanners, require the implementation of a special sequence using the navigator echo method followed by a correction algorithm of the raw data in Fourier space. Initial results showed a significant improvement in the differentiation of healthy and pathologic tissue classes.