TY - GEN
T1 - MoCoSR
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
AU - Zhang, Weitong
AU - Basaran, Berke
AU - Meng, Qingjie
AU - Baugh, Matthew
AU - Stelter, Jonathan
AU - Lung, Phillip
AU - Patel, Uday
AU - Bai, Wenjia
AU - Karampinos, Dimitrios
AU - Kainz, Bernhard
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Abdominal MR
KW - Deep Learning
KW - Motion Correction
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85174680780&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43999-5_12
DO - 10.1007/978-3-031-43999-5_12
M3 - Conference contribution
AN - SCOPUS:85174680780
SN - 9783031439988
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 131
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 October 2023 through 12 October 2023
ER -