MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels

TL;DR

This paper introduces a self-supervised deep variational method for MRI parameter mapping that models pixel dependencies, resulting in sharper, more reliable tissue maps and improving upon traditional independent-pixel assumptions.

Contribution

The authors propose a novel Gaussian mixture VAE framework that breaks the independent pixel assumption, leveraging data redundancies for improved MRI parameter mapping.

Findings

Outperforms traditional model fitting in simulations and real data.

Produces sharper, more detailed quantitative maps.

Enhances the clinical applicability of MRI parameter mapping.

Abstract

We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps are calculated by fitting a model to multiple images, e.g. with least-squares or machine learning. However, the overwhelming majority of model fitting techniques assume that each voxel is independent, ignoring any co-dependencies in the data. This makes model fitting sensitive to voxelwise measurement noise, hampering reliability and repeatability. We propose a self-supervised deep variational approach that breaks the assumption of independent pixels, leveraging redundancies in the data to effectively perform data-driven regularisation of quantitative maps. We demonstrate that our approach outperforms current model fitting techniques in dMRI simulations and real data. Especially with a Gaussian mixture prior, our model enables sharper quantitative maps, revealing finer anatomical details that are not presented in the baselines. Our approach can hence support the clinical adoption of parameter mapping methods such as dMRI and qMRI.