A Regularization Method Based on the Correlations of Diffusion Weighted Images

Diffusion weighted images are affected by several artifacts and noise which complicate the analysis and interpretation of DTI data. The method we present in this thesis works for the regularization of DTI data, which takes into account all the relations of DWI components, including relations of the values at different encoding directions. Our method is based on the theory of Wiener filter, and we molded it to filter the DWI dataset The method was illustrated by the synthetic and real data and the result has supported the filtering methodology proposed in this thesis.