Abstract #0746

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

Jize Zhang¹, Frederik Lange², Jesper Andersson², Jialan Zheng^3,4, Yi Jing⁵, Hongjia Yang³, Mingxuan Liu³, Zihan Li³, Wenchuan Wu², Qiyuan Tian^3,6, and Ziyu Li²

¹Wellcome Centre for Integrative Neuroimaging, OHBA, Department of Psychiatry, University of Oxford, Oxford, United Kingdom, ²Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ³School of Biomedical Engineering, Tsinghua University, Beijing, China, ⁴Tanwei College, Tsinghua University, Beijing, China, ⁵Department of Computer Science and Technology, Tsinghua University, Beijing, China, ⁶Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China

Synopsis

Keywords: Analysis/Processing, Artifacts, U-Net, eddy current, motion correction

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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