Abstract
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-upervised 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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIRASOL_035.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=dKLjnDYaQ2
BibTex
@InProceedings{KhaSar_LargeScale_MICCAISAT2026,
author = { Khanal, Saroj AND Yadav, Yashawant Kumar AND Bhattarai, Kritam AND Neupane, Jeevan AND Subedi, Shristi AND Gwachha, Saship AND Tiwari, Manish Kumar AND Zhang, Dong AND Raymond, Confidence AND Iorumbur, Aondona Moses AND Anazodo, Udunna C. AND Khanal, Bishesh AND Shakya, Mahesh AND Shrestha, Pralhad Kumar},
title = { { Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17264},
month = {pending},
page = {pending}
}