Towards Super-Resolution CEST MRI for Visualization of Small Structures

• URL: http://arxiv.org/abs/2112.01905v1
• Date: Fri, 3 Dec 2021 13:41:57 GMT
• Title: Towards Super-Resolution CEST MRI for Visualization of Small Structures
• Authors: Lukas Folle, Katharian Tkotz, Fasil Gadjimuradov, Lorenz Kapsner, Moritz Fabian, Sebastian Bickelhaupt, David Simon, Arnd Kleyer, Gerhard Kr\"onke, Moritz Zai{\ss}, Armin Nagel, Andreas Maier
• Abstract summary: The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results in challenging early detection. Modern imaging techniques such as chemical exchange saturation transfer (CEST) MRI drive the hope to improve early detection. CEST MRI suffers from an inherently low resolution due to the underlying physical constraints of the acquisition. We show, that neural networks are able to learn the mapping from low-resolution to high-resolution unsaturated CEST images considerably better than present methods.
• Score: 4.004046600770185
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 32.29dB (+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.

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