MoCoSR: respiratory motion correction and super-resolution for 3D abdominal MRI
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Abdominal MRI is critical for diagnosing a wide variety of diseases. However, due to respiratory motion and other organ motions, it is challenging to obtain motion-free and isotropic MRI for clinical diagnosis. Imaging patients with inflammatory bowel disease (IBD) can be especially problematic, owing to involuntary bowel movements and difficulties with long breath-holds during acquisition. Therefore, this paper proposes a deep adversarial super-resolution (SR) reconstruction approach to address the problem of multi-task degradation by utilizing cycle consistency in a staged reconstruction model. We leverage a low-resolution (LR) latent space for motion correction, followed by super-resolution reconstruction, compensating for imaging artefacts caused by respiratory motion and spontaneous bowel movements. This alleviates the need for semantic knowledge about the intestines and paired data. Both are examined through variations of our proposed approach and we compare them to conventional, model-based, and learning-based MC and SR methods. Learned image reconstruction approaches are believed to occasionally hide disease signs. We investigate this hypothesis by evaluating a downstream task, automatically scoring IBD in the area of the terminal ileum on the reconstructed images and show evidence that our method does not suffer a synthetic domain bias.
Editor(s)
Greenspan, H
Madabhushi, A
Mousavi, P
Salcudean, S
Duncan, J
Syeda-Mahmood, T
Taylor, R
Date Issued
2023-10-01
Date Acceptance
2023-10-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14229, pp.121-131
ISBN
978-3-031-43998-8
ISSN
0302-9743
Publisher
Springer
Start Page
121
End Page
131
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
14229
Copyright Statement
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-43999-5_12
Source
26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Subjects
Abdominal MR
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Deep Learning
DISEASE
Life Sciences & Biomedicine
Motion Correction
Radiology, Nuclear Medicine & Medical Imaging
RECONSTRUCTION
Science & Technology
SEGMENTATION
Super-resolution
Technology
Publication Status
Published
Start Date
2023-10-08
Finish Date
2023-10-12
Coverage Spatial
Vancouver, Canada
Date Publish Online
2023-10-01