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

## TL;DR

This paper introduces an unsupervised clustering method to classify water populations in brain tissue using MRI data, revealing more detailed tissue structures than traditional methods.

## Contribution

The novel use of unsupervised clustering in diffusion-relaxation MRI to classify tissue water populations without pre-defined parameters.

## Key findings

- Unsupervised clustering successfully separates white matter, gray matter, and free water in rat and human brains.
- Additional water populations in high cell density regions like the dentate gyrus are identified using high frequency-dependent diffusion data.
- The method can be applied to other body parts like the prostate and breast without requiring pre-defined bin limits.

## Abstract

Massively multidimensional diffusion-relaxation correlation MRI provides detailed information on tissue microstructure by analyzing water populations at a sub-voxel level. This method correlates frequency-dependent tensor-valued diffusion MRI with longitudinal and transverse relaxation rates, generating non-parametric D(ω)-R1-R2-distributions. Traditionally, D(ω)-R1-R2-distributions are separated using manual binning of the diffusivity and anisotropy space to differentiate white matter (WM), gray matter (GM), and free water (FW) in brain tissue. However, while effective, this approach oversimplifies complex tissue fractions and does not fully utilize all available diffusion-relaxation parameters. In this study, we implemented an unsupervised clustering approach to automatically classify WM, GM, and FW and explore additional water populations using all components in the D(ω)–R1-R2-distributions on ex vivo and in vivo rat brain, and in vivo human brain. Results showed that a basic separation of WM, GM, and FW is possible using unsupervised clustering even under different multidimensional diffusion-relaxation protocols of rat brain and human brain. Additionally, when there is high frequency-dependent diffusion range, it is possible to obtain a cluster characterized by restriction localized in specific high cell density regions such as the dentate gyrus and cerebellum of rat brain. These findings were compared with rat histological sections of myelin and Nissl stainings. We demonstrated that unsupervised clustering of diffusion-relaxation MRI data can reveal tissue complexity beyond traditional WM, GM, and FW segmentation in rat and human brain without parameter assumptions. The unsupervised cluster approach could be used in other body parts (e.g., prostate and breast cancer) without requiring pre-defined bin limits. Furthermore, the characterization of the clusters by diffusivities, anisotropy, and relaxation rates can provide a better understanding of the subtle changes in different cellular fractions in tissue-specific pathologies.