Microscopic Propagator Imaging (MPI) with Diffusion MRI

TL;DR

This paper introduces Microscopic Propagator Imaging (MPI), a new diffusion MRI technique that directly assesses microstructural features in nervous tissue, independent of tissue organization, using spherical harmonics, simulation, and machine learning.

Contribution

MPI provides microstructure-specific indices in diffusion MRI, overcoming limitations of traditional methods by being independent of tissue orientation dispersion.

Findings

MPI indices are more specific to microstructural properties.

Demonstrated on synthetic and human data.

Outperforms traditional diffusion metrics in microstructure detection.

Abstract

We propose Microscopic Propagator Imaging (MPI) as a novel method to retrieve the indices of the microscopic propagator which is the probability density function of water displacements due to diffusion within the nervous tissue microstructures. Unlike the Ensemble Average Propagator indices or the Diffusion Tensor Imaging metrics, MPI indices are independent from the mesoscopic organization of the tissue such as the presence of multiple axonal bundle directions and orientation dispersion. As a consequence, MPI indices are more specific to the volumes, sizes, and types of microstructures, like axons and cells, that are present in the tissue. Thus, changes in MPI indices can be more directly linked to alterations in the presence and integrity of microstructures themselves. The methodology behind MPI is rooted on zonal modeling of spherical harmonics, signal simulation, and machine learning regression, and is demonstrated on both synthetic and Human Diffusion MRI data.