Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
CoRR(2024)
摘要
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.
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