What type of study is this?

This is a Experimental Study study.

September 17, 2025

DeepEddy: high-quality fast eddy current and bulk motion correction using deep learning-based image synthesis and co-registration

Key Points

DeepEddy reduces variance in diffusion volumes by effectively correcting eddy currents and bulk motion.
Achieving performance comparable to traditional methods, DeepEddy eliminates the need for multiple diffusion directions.
The approach involves synthesizing b=0 images and applying non-linear co-registration to improve data accuracy.
This method could significantly impact clinical settings, especially where scan time is constrained.

Abstract

Motivation: FSL's "Eddy" function accurately corrects eddy currents and bulk motion in diffusion data but requires 16 diffusion directions or more. Goal(s): Develop a deep learning-based correction method with Eddy-level performance without the diffusion direction sampling requirement. Approach: Our proposed DeepEddy pipeline 1) converts each diffusion-weighted image (DWI) into a b=0 image; 2) nonlinearly co-registers the synthesized and empirical b=0 images; 3) applies derived warp fields to original correspondence DWIs. Results: DeepEddy reduces diffusion volumes variance, improves diffusion metrics, and achieves Eddy-level performance without the diffusion direction sampling requirement. Impact: DeepEddy enables eddy current and bulk motion correction for diffusion data with any number of diffusion directions, showing the promise to benefit clinical applications where scan time is extremely limited.

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