Towards Super-resolution CEST MRI for Visualization of Small Structures

Folle L, Tkotz K, Gadjimuradov F, Kapsner L, Fabian M, Bickelhaupt S, Simon D, Kleyer A, Krönke G, Zaiß M, Nagel AM, Maier A (2022)

Publication Language: English

Publication Status: Accepted

Publication Type: Book chapter / Article in edited volumes

Future Publication Type: Article in Edited Volumes

Publication year: 2022

Publisher: Springer Vieweg

Edited Volumes: Bildverarbeitung für die Medizin 2022

Series: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26–28, 2022

City/Town: Wiesbaden

Pages Range: 210-215

Event location: Heidelberg

ISBN: 978-3-658-36932-3

DOI: 10.1007/978-3-658-36932-3_45

Open Access Link: https://arxiv.org/abs/2112.01905

Abstract

The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results in challenging early detection of the disease. However, characteristic changes in the anatomy can be detected using imaging techniques such as MRI or CT. Modern imaging techniques such as chemical exchange saturation transfer (CEST) MRI drive the hope to improve early detection even further through the imaging of metabolites in the body. To image small structures in the joints of patients, typically one of the first regions where changes due to the disease occur, a high resolution for the CEST MR imaging is necessary. Currently, however, CEST MR suffers from an inherently low resolution due to the underlying physical constraints of the acquisition. In this work we compared established up-sampling techniques to neural network-based super-resolution approaches. We could show, that neural networks are able to learn the mapping from low-resolution to high-resolution unsaturated CEST images considerably better than present methods. On the test set a PSNR of 32.29 dB (+10%), a NRMSE of 0.14 (+28%), and a SSIM of 0.85 (+15%) could be achieved using a ResNet neural network, improving the baseline considerably. This work paves the way for the prospective investigation of neural networks for super-resolution CEST MRI and, followingly, might lead to a earlier detection of the onset of rheumatic diseases.

Authors with CRIS profile

Lukas Folle Lehrstuhl für Informatik 5 (Mustererkennung) Katharina Tkotz Max Schaldach-Stiftungsprofessur für Biomedizinische Technik Fasil Gadjimuradov Lehrstuhl für Informatik 5 (Mustererkennung) Lorenz Kapsner Institute of Radiology Moritz Fabian Professur für Multimodale Bildgebung in der klinischen Forschung David Simon Department of Medicine 3 – Rheumatology and Immunology Arnd Kleyer Department of Medicine 3 – Rheumatology and Immunology Gerhard Krönke Professur für Modulation von Immunantworten Moritz Zaiß Professur für Multimodale Bildgebung in der klinischen Forschung Armin Michael Nagel Professur für metabolische und funktionelle MR-Bildgebung Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE)

How to cite

APA:

Folle, L., Tkotz, K., Gadjimuradov, F., Kapsner, L., Fabian, M., Bickelhaupt, S.,... Maier, A. (2022). Towards Super-resolution CEST MRI for Visualization of Small Structures. In Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, ThomasTolxdorff (Eds.), Bildverarbeitung für die Medizin 2022. (pp. 210-215). Wiesbaden: Springer Vieweg.

MLA:

Folle, Lukas, et al. "Towards Super-resolution CEST MRI for Visualization of Small Structures." Bildverarbeitung für die Medizin 2022. Ed. Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, ThomasTolxdorff, Wiesbaden: Springer Vieweg, 2022. 210-215.

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