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

arXiv: https://arxiv.org/abs/2411.10772 · HTML: https://mlsanity.com/paper/2411.10772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation](https://mlsanity.com/paper/2205.05587/) (2022)
- [Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning](https://mlsanity.com/paper/2210.02349/) (2022)

## Built on by (most cited first, 1 of 1)

- [Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization](https://mlsanity.com/paper/2512.03196/) (2025)

## Abstract (arXiv)

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.