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.
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References
Gastrointestinal Unit Medical Services MGH, Andres, P.G., Friedman, L.S., et al.: Epidemiology and the natural course of inflammatory bowel disease. Gastroenterol. Clin. North Am. 28(2), 255–281 (1999)
Sandler, R., Eisen, G.: Epidemiology of inflammatory bowel disease. In: Kirsner (ed.) Inflammatory Bowel Disease, p. 96 5th ed. WB Saunders, Philadelphia (2000)
Rosen, M.J., Dhawan, A., Saeed, S.A.: Inflammatory bowel disease in children and adolescents. JAMA Pediatrics. 169(11), 1053–60 (2015)
Tielbeek, J.A., et al.: Grading Crohn disease activity with MRI: interobserver variability of MRI features, MRI scoring of severity, and correlation with Crohn disease endoscopic index of severity. AJR 201(6), 1220–8 (2013)
Ebner, M., et al.: Point-spread-function-aware slice-to-volume registration: application to upper abdominal MRI super-resolution. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 3–13. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52280-7_1
Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: a complex problem with many partial solutions. Magn. Reson. Imaging. 42(4), 887–901 (2015)
Afaq, A., et al.: Pitfalls on PET/MRI. In: Seminars in Nuclear Medicine, vol. 51, pp. 529–39. Elsevier (2021)
Alansary, A., et al.: PVR: patch-to-volume reconstruction for large area motion correction of fetal MRI. IEEE Trans. Med. Imaging. 36(10), 2031–44 (2017)
Wang, Z., Chen, J., Hoi, S.C.H.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365–87 (2021)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee K.: Enhanced deep residual networks for single image super-resolution. In: CVPR, pp. 136–44 (2017)
Feng, C.-M., Yan, Y., Fu, H., Chen, L., Xu, Y.: Task transformer network for joint MRI reconstruction and super-resolution. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part VI. LNCS, vol. 12906, pp. 307–317. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_30
Feng, C.-M., Fu, H., Yuan, S., Xu, Y.: Multi-contrast MRI super-resolution via a multi-stage integration network. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part VI. LNCS, vol. 12906, pp. 140–149. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_14
Chen, Y., Shi, F., Christodoulou, A.G., Xie, Y., Zhou, Z., Li, D.: Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part I. LNCS, vol. 11070, pp. 91–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_11
Sánchez, I., Vilaplana, V.: Brain MRI super-resolution using 3D generative adversarial networks. arXiv preprint arXiv:1812.11440 (2018)
Georgescu, M.I., et al.: Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2195–205 (2023)
Zhao, M., Wei, Y., Wong, K.K.: A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images. Magn. Reson. Imaging. 85, 153–60 (2022)
Do, H., Bourdon, P., Helbert, D., Naudin, M., Guillevin, R.: 7T MRI super-resolution with Generative Adversarial Network. Electronic Imaging. 2021(18), 106–1 (2021)
Liu, J., Li, H., Huang, T., Ahn, E., Razi, A., Xiang, W.: Unsupervised representation learning for 3D MRI super resolution with degradation adaptation. arXiv preprint arXiv:2205.06891 (2022)
Wang, S., et al.: Joint motion correction and super resolution for cardiac segmentation via latent optimisation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 14–24. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_2
Luo, Z., Huang, H., Yu, L., Li, Y., Fan, H., Liu, S.: Deep constrained least squares for blind image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17642–17652 (2022)
Mahapatra, D., Schüffler, P.J., Tielbeek, J.A., Makanyanga, J.C., Stoker, J., Taylor, S.A., et al.: Automatic detection and segmentation of Crohn’s disease tissues from abdominal MRI. IEEE Trans. Med. Imaging. 32(12), 2332–47 (2013)
Holland, R., Patel, U., Lung, P., Chotzoglou, E., Kainz, B.: Automatic detection of bowel disease with residual networks. In: Rekik, I., Adeli, E., Park, S.H. (eds.) PRIME 2019. LNCS, vol. 11843, pp. 151–159. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32281-6_16
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Taylor, S.A., et al.: Diagnostic accuracy of magnetic resonance enterography and small bowel ultrasound for the extent and activity of newly diagnosed and relapsed Crohn’s disease (METRIC): a multicentre trial. Lancet Gastroenterol Hepatol. 3(8), 548–58 (2018)
Romano, Y., Isidoro, J., Milanfar, P.: RAISR: rapid and accurate image super resolution. IEEE Trans. Comput. Imaging. 3(1), 110–25 (2016)
Acknowledgements
This work was supported by the JADS programme at the UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare (EP/S023283/1) and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b143dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) - 440719683. Support was also received by the ERC - project MIA-NORMAL 101083647 and DFG KA 5801/2-1, INST 90/1351-1.
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Zhang, W. et al. (2023). MoCoSR: Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_12
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