Towards Super-Resolution CEST MRI for Visualization of Small Structures
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

TL;DR
This paper demonstrates that neural network-based super-resolution significantly improves the quality of CEST MRI images, potentially enabling earlier detection of rheumatic diseases by visualizing small joint structures.
Contribution
It introduces neural network approaches, specifically ResNet, for super-resolution of CEST MRI, outperforming traditional up-sampling methods in image quality metrics.
Findings
Neural networks outperform traditional methods in super-resolution of CEST MRI.
ResNet achieved a PSNR of 32.29dB, NRMSE of 0.14, SSIM of 0.85.
Improved image quality may facilitate earlier detection of rheumatic diseases.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Average Pooling · Max Pooling · Residual Connection · Batch Normalization · 1x1 Convolution · Bottleneck Residual Block · Global Average Pooling · Kaiming Initialization · Convolution
