Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
606-02-005 ISMRM Abstract

A Deep-Learning-Based Label-Free No-Reference Image Quality Assessment Metric Evaluated with Proton and Sodium MRI

Primary: Analysis Methods - Data Processing
Secondary: Contrast Mechanisms - Non-Proton
606-02-005 · Beyond Protons: Advances in Multinuclear and Hyperpolarized MRI · Thursday, 14 May, 1:40 PM–3:30 PM · Auditorium 2
Keywords: Deep learning Image Quality Assessment Sodium MRI
Accepted
Shuaiyu Yuan 1, Tristan Whitmarsh2, Dimitri Kessler1, Otso Arponen1, Mary A McLean1, Gabrielle Baxter3, Frank Riemer4, Aneurin J Kennerley5,6, William Brackenbury5,7, Fiona Gilbert1, Joshua D Kaggie1
1Department of Radiology, University of Cambridge, Cambridge, United Kingdom
2Department of Engineering, University of Cambridge, Cambridge, United Kingdom
3Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
4Helse Bergen, Bergen, Norway
5Department of Biology, University of York, York, United Kingdom
6Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester, United Kingdom
7York Biomedical Research Institute, University of York, York, United Kingdom
Presenting Author: Shuaiyu Yuan

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References

1. Stepien Igor, Oszust Mariusz. A brief survey on no-reference image quality assessment methods for magnetic resonance images. Journal of Imaging. 2022;8(6):160. https://doi.org/10.3390/jimaging8060160 [doi]
2. Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
3. J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool and R. Timofte, "SwinIR: Image Restoration Using Swin Transformer," 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021, pp. 1833-1844, doi: 10.1109/ICCVW54120.2021.00210. [doi]
4. Kaggie Joshua D, Baxter Gabrielle C, McLean Mary A, et al. Sodium Breast Imaging of Ductal Carcinomas at 3T. ISMRM2022. 2022.
5. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS '20). Curran Associates Inc., Red Hook, NY, USA, Article 574, 6840–6851.
6. K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering," in IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, Aug. 2007, doi: 10.1109/TIP.2007.901238. [doi]
7. K. Zhang, W. Zuo, Y. Chen, D. Meng and L. Zhang, "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising," in IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155, July 2017, doi: 10.1109/TIP.2017.2662206. [doi]
8. Z. Zhang, Q. Liu and Y. Wang, "Road Extraction by Deep Residual U-Net," in IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 749-753, May 2018, doi: 10.1109/LGRS.2018.2802944. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/606-02-005