An information theoretic approach characterizing diffusion anisotropy in diffusion-weighted magnetic resonance images

Abstract

We propose an alternative approach that does not rely on tensor models for characterizing diffusion anisotropy from diffusion-weighted magnetic resonance images. Information content inherent in the diffusion attenuation values are the only measures needed for our characterization. We explore the information content inherent in these values. We calculate Shannon's entropy on the diffusion attenuation values measured across the applied diffusion-sensitizing gradient directions. This method is evaluated with data generated with different diffusion gradient encoding schemes demonstrating the validity of our approach and its potential use to better differentiate between brain tissue types over tensor-based measures.