Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-03-181 ISMRM Abstract

Zero‑Shot Low‑Field MRI Quality Enhancement Using a Noise Level Adaptive Diffusion Model (Nila)

Primary: Acquisition & Reconstruction - AI methods
Secondary: Physics & Engineering - Low-Field MRI
570-03-181 · AI Methods · Wednesday, 13 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Low-Field MRI Diffusion model Zero-shot
Accepted
Jiacai Cai1,2, Nanxiong Liu1, Shoujin Huang1, Yuwan Wang1,2, Yansong Bu1,2, Zihao Wang1,2, Shaojun Liu1,2, Min Wang 3,4, Mengye Lyu1,2
1Shenzhen Technology University, Shenzhen, China
2Shenzhen University, Shenzhen, China
3Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
4Department of Endocrinology, School of Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
Presenting Author: Min Wang

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References

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2. Marques JP, Simonis FFJ, Webb AG. Low-field MRI: an MR physics perspective. J Magn Reson Imaging. 2019;49(6):1528-1542. doi:10.1002/jmri.26637. [doi]
3. Kravchenko D, Hagar MT, Vecsey-Nagy M, et al. Low-field and portable MRI technology: advancements and innovations. Eur Radiol Exp. 2025;9:103. doi:10.1186/s41747-025-00638-2. [doi]
4. 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 (LoHiResGAN). Sci Rep. 2023;13:21183. doi:10.1038/s41598-023-48438-1. [doi]
5. Lin H, Figini M, Tanno R, et al. Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator. In: Machine Learning for Medical Image Reconstruction (MLMIR 2019). LNCS 11905. Springer; 2019:58-70. doi:10.1007/978-3-030-33843-5_6. [doi]
6. Chen Y, Konz N, Gu H, et al. ContourDiff: Unpaired Image-to-Image Translation with Structural Consistency for Medical Imaging. arXiv:2403.10786, 2024. doi:10.48550/arXiv.2403.10786 [doi]
7. Huang S, Luo G, Wang X, et al. Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI. In: MICCAI 2024. LNCS 15007. Springer, Cham; 2024:498-508. doi:10.1007/978-3-031-72104-5_48. [doi]
8. Dabov K, Foi A, Katkovnik V, Egiazarian K. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering (BM3D). IEEE TIP, 2007;16(8):2080–2095. doi: 10.1109/TIP.2007.901238. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/570-03-181