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

Weitong Zhang, Berke Doga Basaran, Qingjie Meng, Matthew Baugh, Jonathan K. Stelter, Phillip Lung, Uday Patel, Wenjia Bai, Dimitrios C. Karampinos, Bernhard Kainz

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.