# DrDisco: Deep Registration for Distortion Correction of Diffusion MRI with single phase-encoding

arXiv: https://arxiv.org/abs/2304.00217 · HTML: https://mlsanity.com/paper/2304.00217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2023.

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

- [Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction](https://mlsanity.com/paper/2606.18869/) (2026)

## Abstract (arXiv)

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geometric distortions that interfere with further analyses. Most tools for correcting distortion require two minimally weighted DW-MRI images (B0) acquired with different phase-encoding directions, and they can take hours to process per subject. Since a great amount of diffusion data are only acquired with a single phase-encoding direction, the application of existing approaches is limited. We propose a deep learning-based registration approach to correct distortion using only the B0 acquired from a single phase-encoding direction. Specifically, we register undistorted T1-weighted images and distorted B0 to remove the distortion through a deep learning model. We apply a differentiable mutual information loss during training to improve inter-modality alignment. Experiments on the Human Connectome Project dataset show the proposed method outperforms SyN and VoxelMorph on several metrics, and only takes a few seconds to process one subject.