Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
351-03-005 / 351-03-005 ISMRM Abstract

Learning Beyond Interpolation: Zero-shot Resolution Enhancement for Low-Field MRI

Accepted
Ajay Sharma1, Sairam Geethanath1
1Johns Hopkins University School of Medicine, Baltimore, United States of America
Presenting Author: Toufiq Musah

Synopsis

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References

1. Samardzija A, Selvaganesan K, Zhang HZ, et al. Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging. Annu Rev Biomed Eng. 2024;26(1). doi:10.1146/annurev-bioeng-110122-022903 2. Baljer L, Zhang Y, Bourke NJ, et al. Ultra-Low-Field Paediatric MRI in Low- and Middle-Income Countries: Super-Resolution Using a Multi-Orientation U-Net. Hum Brain Mapp. 2025;46(1). doi:10.1002/hbm.70112 3. Csébfalvi B. Beyond trilinear interpolation. ACM Trans Graph. 2019;38(4). doi:10.1145/3306346.3323032 4. Iglesias JE, Schleicher R, Laguna S. Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning. Radiology. 2023;306. 5. Iglesias JE, Schleicher R, Laguna S, et al. Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning. Radiology. 2023;306(3). doi:10.1148/radiol.220522 6. Lucas A, Campbell Arnold T, Okar S V, et al. Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data. medRxiv. Published online 2023. doi:10.1101/2023.12.28.23300409 7. Arnold TC, Freeman CW, Litt B, Stein JM. Low-field MRI: clinical promise and challenges. Journal of Magnetic Resonance Imaging. 2023;57(1):25-44. 8. Geethanath S, Vaughan JT. Accessible magnetic resonance imaging: A review. Journal of Magnetic Resonance Imaging. 2019;49(7). doi:10.1002/jmri.26638 9. Batson J, Royer L. Noise2Seif: Blind denoising by self-supervision. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. 2019. 10. Shocher A, Cohen N, Irani M. Zero-shot’’ super-resolution using deep internal learning. In: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition. 2018:3118-3126. 11. Qiao C, Zeng Y, Meng Q, et al. Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy. Nat Commun. 2024;15(1). doi:10.1038/s41467-024-48575-9 12. Wang Z, Chen J, Hoi SCH. Deep Learning for Image Super-Resolution: A Survey. IEEE Trans Pattern Anal Mach Intell. 2021;43(10). doi:10.1109/TPAMI.2020.2982166 13. Islam KT, Zhong S, Zakavi P, et al. Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images. Sci Rep. 2023;13(1). doi:10.1038/s41598-023-48438-1 14. Billot B, Greve DN, Puonti O, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal. 2023;86:102789. doi:10.1016/j.media.2023.102789 15. Shocher A, Cohen N, Irani M. “zero-shot” super-resolution using deep internal learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:3118-3126. 16. Abrol M, Shah MD, Singh AP. An Overview of Interpolation Algorithms for Image Super-Resolution. In: 2024 3rd International Conference for Advancement in Technology(ICONAT). IEEE; 2024:1-5. 17. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545-563. 18. Sharma A, Mishra PK. DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images. Neural Comput Appl. 2023;35(26):19187-19220. 19. Ssentamu T, Kimbowa A, Omoding R, et al. Denoising very low-field magnetic resonance images using native noise modeling. Frontiers in Neuroimaging. 2025;4. doi:10.3389/fnimg.2025.1501801 [doi]

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