Characterizing microstructure of living tissues with time-dependent diffusion

TL;DR

This paper introduces a framework to relate diffusion measurements to tissue microstructure by classifying structural complexity via the long time tail exponent in the velocity autocorrelation function, aiding in understanding living tissues and disease states.

Contribution

It presents a novel classification method linking diffusion metrics to microstructural features using the dynamical exponent, applicable to MRI data in tissues and pathological conditions.

Findings

Identifies tissue microanatomy affecting water diffusion in muscles and brain.

Detects microstructural changes in ischemic stroke.

Provides a systematic approach to interpret diffusion measurements.

Abstract

Molecular diffusion measurements are widely used to probe microstructure in materials and living organisms noninvasively. The precise relation of diffusion metrics to microstructure remains a major challenge: In complex samples, it is often unclear which structural features are most relevant and can be quantified. Here we classify the structural complexity in terms of the long time tail exponent in the molecular velocity autocorrelation function. The specific values of the dynamical exponent let us identify the relevant tissue microanatomy affecting water diffusion measured with MRI in muscles and in brain, and the microstructural changes in ischemic stroke. Our framework presents a systematic way to identify the most relevant part of structural complexity using transport measured with a variety of techniques.