# Model-Informed Machine Learning for Multi-component T2 Relaxometry

arXiv: https://arxiv.org/abs/2007.10225 · HTML: https://mlsanity.com/paper/2007.10225/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook](https://mlsanity.com/paper/2303.02063/) (2023)
- [Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation](https://mlsanity.com/paper/2205.05587/) (2022)
- [P2T2: a Physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI](https://mlsanity.com/paper/2212.04928/) (2022)
- [Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning](https://mlsanity.com/paper/2606.02044/) (2026)
- [Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers](https://mlsanity.com/paper/2609.39184/) (2026)

## Abstract (arXiv)

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with textita priori knowledge of textitin vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to non-parametric and parametric approaches on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than non-parametric and parametric methods, respectively.