Design of optimal experimental parameters for diffusion-weighted MRI fibre-tracking protocols

Abstract

Diffusion-weighted imaging (DWI) has been shown to provide valuable structural information in biological fibrous soft tissues, such as the central and peripheral nervous system and skeletal muscle, based on the acquisition of a series of images that sample q-space. Existing q-space sampling schemes have been compared using several metrics based on diffusion tensor imaging (DTI), which models the restricted diffusion inside fibres as anisotropic unrestricted diffusion. Otherwise, sampling schemes have been optimized using model-independent cost functions. We propose using a model-based framework that connects the experimental noise, the uncertainty in the estimation of the model parameters, and the q-space sampling schemes together, to obtain model-based optimal sampling schemes for the case when the fibre orientation is known a priori to be within a finite region. We apply and validate this framework for the recently proposed quantitative analysis of q-space MRI data (QUAQ) model.