Susceptibility Distortion Correction of Diffusion MRI with a single Phase-Encoding Direction

TL;DR

This paper introduces a deep learning method to correct susceptibility distortions in diffusion MRI using only a single phase-encoding direction, offering a practical alternative to traditional paired-image correction techniques.

Contribution

The proposed approach enables susceptibility distortion correction with a single acquisition, removing the need for paired images and improving practicality for retrospective data analysis.

Findings

Performance comparable to traditional methods like topup

Effective correction with only one phase-encoding direction

Potential for retrospective and real-time applications

Abstract

Diffusion MRI (dMRI) is a valuable tool to map brain microstructure and connectivity by analyzing water molecule diffusion in tissue. However, acquiring dMRI data requires to capture multiple 3D brain volumes in a short time, often leading to trade-offs in image quality. One challenging artifact is susceptibility-induced distortion, which introduces significant geometric and intensity deformations. Traditional correction methods, such as topup, rely on having access to blip-up and blip-down image pairs, limiting their applicability to retrospective data acquired with a single phase encoding direction. In this work, we propose a deep learning-based approach to correct susceptibility distortions using only a single acquisition (either blip-up or blip-down), eliminating the need for paired acquisitions. Experimental results show that our method achieves performance comparable to topup, demonstrating its potential as an efficient and practical alternative for susceptibility distortion correction in dMRI.