Abstract
Aim of this study was to analyse diffusion tensor imaging (DTI) datasets in order to identify damaged areas or disorders of the brain in a semi-automatic way. For this purpose, a software tool has been developed: it takes in input the fractional anisotropy (FA) map of a (damaged) brain and, after several steps involving the comparison between the two brain hemispheres, it gives back, as output, a binary mask with a ROI (Region of Interest) that shows the probably damaged area. In the same way, starting from the MR image without diffusion weighting (b0), we find another ROI that we compare with the one previously detected from the FA map. Then we overlay these ROIs onto both the FA map and the image without diffusion weighting, trying to quantify how well the ROIs cover the pathological tissue. This procedure was repeated on a few patients (healthy and pathological ones). The algorithm worked well, showing as a preliminary result that FA maps allow a neater detection of the pathological tissue if compared to MR images without diffusion weighting.
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