Ivan Maximov1, Farida Grinberg1, N. Jon Shah1, 2
1Institute of Neuroscience and Medicine 4, Forschungszentrum Juelich GmbH, Juelich, Germany; 2RWTH Aachen University, Department of Neurology, Faculty of Medicine, JARA, Aachen, Germany
Low redundancy DTI data sets are a rather complicated problem for diffusion tensor estimation, especially in a clinical human brain imaging. The diffusion signal attenuations are frequently corrupted by a physiological noise such as a cardiac pulsation, bulk head motion, respiratory motion, etc. As a consequence, diffusion tensor estimation becomes unstable and very poor. We have developed fitting algorithms based on the least trimmed squares and the median absolute deviation robust estimators in order to improve the tensor assessment in small DTI samples.