Legouhy, A;
Graham, M;
Guerreri, M;
Stee, W;
Villemonteix, T;
Peigneux, P;
Zhang, H;
(2022)
Correction of Susceptibility Distortion in EPI: A Semi-supervised Approach with Deep Learning.
Lecture Notes in Computer Science
, 13722
pp. 38-49.
10.1007/978-3-031-21206-2_4.
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Abstract
Echo planar imaging (EPI) is the most common approach for acquiring diffusion and functional MRI data due to its high temporal resolution. However, this comes at the cost of higher sensitivity to susceptibility-induced B0 field inhomogeneities around air/tissue interfaces. This leads to severe geometric distortions along the phase encoding direction (PED). To correct this distortion, the standard approach involves an analogous acquisition using an opposite PED leading to images with inverted distortions and then non-linear image registration, with a transformation model constrained along the PED, to estimate the voxel-wise shift that undistorts the image pair and generates a distortion-free image. With conventional image registration approaches, this type of processing is computationally intensive. Recent advances in unsupervised deep learning-based approaches to image registration have been proposed to drastically reduce the computational cost of this task. However, they rely on maximizing an intensity-based similarity measure, known to be suboptimal surrogate measures of image alignment. To address this limitation, we propose a semi-supervised deep learning algorithm that directly leverages ground truth spatial transformations during training. Simulated and real data experiments demonstrate improvement to distortion field recovery compared to the unsupervised approach, improvement image similarity compared to supervised approach and precision similar to TOPUP but with much faster processing.
| Type: | Article |
|---|---|
| Title: | Correction of Susceptibility Distortion in EPI: A Semi-supervised Approach with Deep Learning |
| ISBN-13: | 9783031212055 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-21206-2_4 |
| Publisher version: | https://doi.org/10.1007/978-3-031-21206-2_4 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Deep learning registration, Susceptibility distortion correction, Semi-supervised learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10162752 |
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