Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning

TL;DR

This paper introduces Eddeep, a deep learning-based method for fast eddy-current distortion correction in diffusion MRI, achieving comparable results to traditional tools with reduced computational cost and potential for real-time processing.

Contribution

The paper presents the first deep learning approach for eddy-current distortion correction in diffusion MRI, offering a faster alternative to existing methods like FSL Eddy.

Findings

Comparable distortion estimates to FSL Eddy

Requires only modest training sample sizes

Enables potential for real-time MRI preprocessing

Abstract

Modern diffusion MRI sequences commonly acquire a large number of volumes with diffusion sensitization gradients of differing strengths or directions. Such sequences rely on echo-planar imaging (EPI) to achieve reasonable scan duration. However, EPI is vulnerable to off-resonance effects, leading to tissue susceptibility and eddy-current induced distortions. The latter is particularly problematic because it causes misalignment between volumes, disrupting downstream modelling and analysis. The essential correction of eddy distortions is typically done post-acquisition, with image registration. However, this is non-trivial because correspondence between volumes can be severely disrupted due to volume-specific signal attenuations induced by varying directions and strengths of the applied gradients. This challenge has been successfully addressed by the popular FSL~Eddy tool but at considerable computational cost. We propose an alternative approach, leveraging recent advances in image processing enabled by deep learning (DL). It consists of two convolutional neural networks: 1) An image translator to restore correspondence between images; 2) A registration model to align the translated images. Results demonstrate comparable distortion estimates to FSL~Eddy, while requiring only modest training sample sizes. This work, to the best of our knowledge, is the first to tackle this problem with deep learning. Together with recently developed DL-based susceptibility correction techniques, they pave the way for real-time preprocessing of diffusion MRI, facilitating its wider uptake in the clinic.