# Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging

arXiv: https://arxiv.org/abs/2609.06437 · HTML: https://mlsanity.com/paper/2609.06437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 6 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset](https://mlsanity.com/paper/2306.16925/) (2023)
- [cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis](https://mlsanity.com/paper/2411.17203/) (2024)
- [Paired Image-to-Image Translation Quality Assessment Using Multi-Method Fusion](https://mlsanity.com/paper/2205.04186/) (2022)
- [Susceptibility Distortion Correction of Diffusion MRI with a single Phase-Encoding Direction](https://mlsanity.com/paper/2508.13340/) (2025)

## Abstract (arXiv)

Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods often require additional acquisitions or vendor-specific solutions, limiting their feasibility in high-throughput, resource-constrained settings. This study investigates whether large-scale pretraining strategies can improve deep learning-based distortion correction for single-phase-encoding DWI. We formulate the task as image reconstruction, and compare a non-pretrained baseline against a self-supervised and a generative pretrained model, evaluated using both quantitative image-similarity metrics and qualitative expert assessment. The best-performing model was further tested for transferability on data collected in an LMIC setting with acquisition shift. Pretrained models outperformed the non-pretrained baseline, with cWDM achieving the strongest results across both quantitative and qualitative evaluation. However, application to LMIC data revealed transferability challenges, including contrast alteration and over-reliance on T1-weighted anatomical structure. Registering images to a common standard space improved predictions, suggesting that harmonized preprocessing may enhance cross-domain deployment.