Simulated Dataset for Verification & Validation of DT-MRI Analyzing Tools

In diffusion tensor MRI (DT-MRI), each voxel is assigned a tensor that describes local water diffusion. In this study, a simulated DT dataset for analyzing the diffusion characteristics is developed to verify and validate DT images postprocessed with various DT analyzing codes. This module is intended as a resource for DT-MRI analyzing tools to verify and validate the analysis results. The b factor in our study is the B matrix of size 1times7. In our sample, 6 diffusion weighted images and a null image namely the T2 image creating a set of intensity images of size 256times256times7 is generated for the analysis. The idea is to fullfil the routine DT analysis from the apparent diffusion coefficient ADC image instead of the DT images. This inverse analysis methodology is preparing the basis of the image information to be investigated as known values. According to the Stejskal Tanner equation, D= [Dxx, Dyy, Dzz, Dxy, Dxz, Dyz] is calculated in the algorithm. After the validation of the algorithm with the simulated diffusion tensor dataset, real MR data of human brain and myocardium are used. The eigensystem D is calculated in every pixel, ADC is represented with respect to D. The other characteristic values of diffusivity namely fractional (FA) and relative (RA) anisotropy values are calculated. Developing a reliable and rapid tractography algorithm for the clinical use regarding to these verified results is the future study of the work in progress