Regularization for MRI Diffusion Inverse Problem

Abstract

In this thesis, we introduce a novel method of reconstructing fibre directions from diffusion images. By modelling the Principal Diffusion Direction PDD (the fibre direction) directly, we are able to apply regularization to the fibre direction explicitly, which was not possible before. Diffusion Tensor Imaging (DTI) is a technique which extracts information from multiple Magnetic Resonance Images about the amount and orientation of diffusion within the body. It is commonly used for brain connectivity studies, providing information about the white matter structure. Many methods have been represented in the literature for estimating diffusion tensors with and without regularization. Previous methods of regularization applied to the source images or diffusion tensors. The process of extracting PDDs therefore required two or three numerical procedures, in which regularization (including filtering) is applied in earlier steps before the PDD is extracted. Such methods require and/or impose smoothness on all components of the signal, which is inherently less efficient than using regularizing terms that penalize non-smoothness in the principal diffusion direction directly. Our model can be interpreted as a restriction of the diffusion tensor model, in which the principal eigenvalue of the diffusion tensor is a model variable and not a derived quantity. We test the model using a numerical phantom designed to test many fibre orientations in parallel, and process a set of thigh muscle diffusion-weighted images.