MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI

Zhang W, Basaran B, Meng Q, Baugh M, Stelter J, Lung P, Patel U, Bai W, Karampinos D, Kainz B (2023)

Publication Type: Conference contribution, Original article

Publication year: 2023

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14229 LNCS

Pages Range: 121-131

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vancouver, BC CA

ISBN: 9783031439988

DOI: 10.1007/978-3-031-43999-5_12

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.

Authors with CRIS profile

Matthew Moses George Baugh
Bernhard Kainz Professur für Image Data Exploration and Analysis

Related research project(s)

Medical Image Analysis with Normative Machine Learning (ERC-CoG MIA-NORMAL) Sept. 1, 2023 - Sept. 30, 2028 End-to-End Deep Learning Image Reconstruction and Pathology Detection Jan. 1, 2023 - Dec. 31, 2025

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Technische Universität München (TUM) DE Germany (DE) London North West Healthcare NHS Trust GB United Kingdom (GB)

How to cite

APA:

Zhang, W., Basaran, B., Meng, Q., Baugh, M., Stelter, J., Lung, P.,... Kainz, B. (2023). MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI. In Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 121-131). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.

MLA:

Zhang, Weitong, et al. "MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI." Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, BC Ed. Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, Springer Science and Business Media Deutschland GmbH, 2023. 121-131.

BibTeX: Download