Limitations of apparent diffusion coefficient-based models in characterizing non-gaussian diffusion.

Abstract

Diffusion in complex heterogeneous structures, for example, the neural fiber system, is non-gaussian. Recently, several methods have been introduced to address the issue of non-gaussian diffusion in multifiber systems. Some are based on apparent diffusion coefficient (ADC) analysis; and some are based on q-space analysis. Here, using a simple mathematic derivation, ADC-based models are shown to be mathematically self-inconsistent in the presence of non-gaussian diffusion. Monte Carlo simulation on restricted diffusion is applied to demonstrate the poor data fitting that can result from ADC-based models. Specific comparisons are performed between two generalized diffusion tensor imaging methods: one of them is based on ADC analysis, and the other is shown to be consistent with q-space formalism. The issue of imaging asymmetric microstructures is also investigated. Signal phase and spin exchange are necessary to resolve multiple orientations of an asymmetric structure.