Data-driven classification of tissue water populations by massively multidimensional diffusion-relaxation correlation MRI

Motivation: Multidimensional diffusion-relaxation MRI opened new ways to non-invasively study sub-voxel populations of water with distinct MRI signal responses and, by inference, tissue microstructure. However, this technique creates large number nonparametric diffusion-relaxation distributions that are challenging to visualize or translate into microstructure specific maps. Goal(s): The goal of this study is to automatically classify the distribution components for an ex vivo rat brain and compare them with histology to reveal their links to tissue fractions. Approach: To achieve the automatic classification, we use an unsupervised data-driven clustering approach. Results: We successfully separated white matter, gray matter, free water and additional tissue fractions. Impact: Multidimensional diffusion-relaxation MRI combined with data-driven microstructure clustering offers new perspectives in high-specificity studies of healthy and damage tissue beyond the conventional white matter, gray matter, and free-water fractions. This is achieved by exploring the full sub-voxel multidimensional distribution space.