Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-002 ISMRM Abstract

Deep Diffusion Prior of Sole High-field Data for Accelerated Low-field and Ultra-low-field MRI

Primary: Physics & Engineering - Low-Field MRI
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
364-04-002 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: Image Reconstruction Low-Field MRI Deep learning
Accepted
Haobo Wang 1,2, Zhuoxu Cui1,3,4, Congcong Liu4, Yuanyuan Liu2, Xingyang Wu2, Shuo Zhou2, Yihang Zhou1,3,4, Dong Liang1,2,3,4, Hector S Lopez4,5,6, Hui Dong7, Haifeng Wang1,2,3
1University of Chinese Academy of Sciences, Beijing, China
2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China
4Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
5Kyoto Future Medical Instruments Inc., Kyoto, Japan
6Human Brain Research Center, Kyoto University, Kyoto, Japan
7The State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
Presenting Author: Haobo Wang

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References

1. Hori, Masaaki, et al. "Low-field magnetic resonance imaging: its history and renaissance." Investigative radiology 56.11 (2021): 669-679.
2. Kravchenko, Dmitrij, et al. "Low-field and portable MRI technology: advancements and innovations." European Radiology Experimental 9.1 (2025): 103.
3. Arnold, Thomas Campbell, et al. "Low‐field MRI: clinical promise and challenges." Journal of Magnetic Resonance Imaging 57.1 (2023): 25-44.
4. Zhao, Yujiao, et al. "Whole-body magnetic resonance imaging at 0.05 Tesla." Science 384.6696 (2024): eadm7168.
5. Marques, José P., Frank FJ Simonis, and Andrew G. Webb. "Low‐field MRI: an MR physics perspective." Journal of magnetic resonance imaging 49.6 (2019): 1528-1542.
6. Webb, Andrew, and Thomas O’Reilly. "Tackling SNR at low-field: a review of hardware approaches for point-of-care systems." Magnetic Resonance Materials in Physics, Biology and Medicine 36.3 (2023): 375-393.
7. Pohmann, Rolf, Nikolai I. Avdievich, and Klaus Scheffler. "Signal‐to‐noise ratio versus field strength for small surface coils." NMR in Biomedicine 37.10 (2024): e5168.
8. Ssentamu, Tonny, et al. "Denoising very low-field magnetic resonance images using native noise modeling." Frontiers in Neuroimaging 4 (2025): 1501801.
9. Shetty, Anup S., et al. "Low-field-strength body MRI: challenges and opportunities at 0.55 T." RadioGraphics 43.12 (2023): e230073.
10. Man, Christopher, et al. "Deep learning enabled fast 3D brain MRI at 0.055 tesla." Science Advances 9.38 (2023): eadi9327.
11. Koonjoo, Neha, et al. "Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction." Scientific reports 11.1 (2021): 8248.
12. Lin, Xiyue, et al. "Zero-Shot Low-Field MRI Enhancement via Denoising Diffusion Driven Neural Representation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2024.
13. Lucas, Alfredo, et al. "Multisequence 3-T image synthesis from 64-mT low-field-strength MRI using generative adversarial networks in multiple sclerosis." Radiology 315.1 (2025): e233529.
14. Barbano, Riccardo, et al. "Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction." IEEE Transactions on Medical Imaging (2025).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-002