# A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification

arXiv: https://arxiv.org/abs/2412.08741 · HTML: https://mlsanity.com/paper/2412.08741/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2024.

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

- [Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset](https://mlsanity.com/paper/2601.05063/) (2026)

## Abstract (arXiv)

Deep learning-based techniques have potential to optimize scan and post-processing times required for MRI-based fat quantification, but they are constrained by the lack of large training datasets. Generative models are a promising tool to perform data augmentation by synthesizing realistic datasets. However no previous methods have been specifically designed to generate datasets for quantitative MRI (q-MRI) tasks, where reference quantitative maps and large variability in scanning protocols are usually required. We propose a Physics-Informed Latent Diffusion Model (PI-LDM) to synthesize quantitative parameter maps jointly with customizable MR images by incorporating the signal generation model. We assessed the quality of PI-LDM's synthesized data using metrics such as the Fr'echet Inception Distance (FID), obtaining comparable scores to state-of-the-art generative methods (FID: 0.0459). We also trained a U-Net for the MRI-based fat quantification task incorporating synthetic datasets. When we used a few real (10 subjects, ~200 slices) and numerous synthetic samples (>3000), fat fraction at specific liver ROIs showed a low bias on data obtained using the same protocol than training data (0.10% at hboxROI1, 0.12% at hboxROI2) and on data acquired with an alternative protocol (0.14% at hboxROI1, 0.62% at hboxROI2). Future work will be to extend PI-LDM to other q-MRI applications.