Ofer Pasternak1, Nir Sochen2, Peter J. Basser3
1Brigham and Women's Hosptial, Harvard Medical School, Boston, MA, United States; 2Tel Aviv University, Israel; 3Section on Tissue Biophysics & Biomimetics (STBB), National Institutes of Health (NIH), Bethesda, MD, United States
Metric selection is an essential step in performing diffusion tensor analysis, and here we investigate the selection effect on the estimation of FA, ADC and volume of mean tensors. We use Monte-Carlo simulations to generate noisy replicates, and compare estimations using a Euclidean and a Log-Euclidean metrics. The Log-Euclidean metric decreases tensor swelling, however, it is found to introduce other types of estimation biases. We find that for the case of thermal MR noise (rician), the swelling effect reduces estimation bias, and conclude that the Euclidean metric is an appropriate selection.