Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-003 / 452-03-003 ISMRM Abstract

Improving Ultra-low-field Cardiac Cine MRI through Transfer Learning

Primary: Acquisition & Reconstruction - AI methods
Secondary: Analysis Methods - Image Enhancement
452-03-003 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-003 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Super-resolution Deep learning Ultra-Low-Field MRI Cardiac Cine MRI 0.05T
Accepted
Vick Lau 1,2, Ye Ding1,2, Shi Su1,2, Jiahao Hu1,2, Junhao Zhang1,2, Alex T. L. Leong1,2, Yujiao Zhao1,2, Ed X Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China
2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Presenting Author: Vick Lau

Synopsis

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References

1. Rajiah PS, François CJ, Leiner T. Cardiac MRI: State of the Art. Radiology. 2023;307(3):e223008.
2. Cooley CZ, McDaniel PC, Stockmann JP, Srinivas SA, Cauley SF, Sliwiak M, Sappo CR, Vaughn CF, Guerin B, Rosen MS, Lev MH, Wald LL. A portable scanner for magnetic resonance imaging of the brain. Nat Biomed Eng. 2021;5(3):229–239.
3. Sheth KN, Mazurek MH, Yuen MM, Cahn BA, Shah JT, Ward A, Kim JA, Gilmore EJ, Falcone GJ, Petersen N, Gobeske KT, Kaddouh F, Hwang DY, Schindler J, Sansing L, Matouk C, Rothberg J, Sze G, Siner J, Rosen MS, Spudich S, Kimberly WT. Assessment of Brain Injury Using Portable, Low-Field Magnetic Resonance Imaging at the Bedside of Critically Ill Patients. JAMA Neurol. 2020;78(1):41–47.
4. O’Reilly T, Teeuwisse WM, de Gans D, Koolstra K, Webb AG. In vivo 3D brain and extremity MRI at 50 mT using a permanent magnet Halbach array. Magnetic Resonance in Medicine. 2021;85(1):495–505.
5. Liu Y, Leong ATL, Zhao Y, Xiao L, Mak HKF, Tsang ACO, Lau GKK, Leung GKK, Wu EX. A low-cost and shielding-free ultra-low-field brain MRI scanner. Nature Communications. 2021;12(1):7238.
6. Zhao Y, Ding Y, Lau V, Man C, Su S, Xiao L, Leong ATL, Wu EX. Whole-body magnetic resonance imaging at 0.05 Tesla. Science. 2024;384(6696):eadm7168.
7. McDaniel PC, Cooley CZ, Stockmann JP, Wald LL. The MR Cap: A single-sided MRI system designed for potential point-of-care limited field-of-view brain imaging. Magn Reson Med. 2019;82(5):1946–1960.
8. Yuen MM, Prabhat AM, Mazurek MH, Chavva IR, Crawford A, Cahn BA, Beekman R, Kim JA, Gobeske KT, Petersen NH, Falcone GJ, Gilmore EJ, Hwang DY, Jasne AS, Amin H, Sharma R, Matouk C, Ward A, Schindler J, Sansing L, de Havenon A, Aydin A, Wira C, Sze G, Rosen MS, Kimberly WT, Sheth KN. Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke. Sci Adv. 2022;8(16):eabm3952.
9. Hoult DI, Richards R. The signal-to-noise ratio of the nuclear magnetic resonance experiment. Journal of Magnetic Resonance (1969). 1976;24(1):71–85.
10. Sarracanie M, Salameh N. Low-Field MRI: How Low Can We Go? A Fresh View on an Old Debate. Frontiers in Physics. 2020;8.
11. Iglesias JE, Schleicher R, Laguna S, Billot B, Schaefer P, McKaig B, Goldstein JN, Sheth KN, Rosen MS, Kimberly WT. Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning. Radiology. 2023;306(3):e220522.
12. Lau V, Xiao L, Zhao Y, Su S, Ding Y, Man C, Wang X, Tsang A, Cao P, Lau GKK, Leung GKK, Leong ATL, Wu EX. Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution. Magn Reson Med. 2023;90(2):400–416.
13. Man C, Lau V, Su S, Zhao Y, Xiao L, Ding Y, Leung GKK, Leong ATL, Wu EX. Deep learning enabled fast 3D brain MRI at 0.055 tesla. Science Advances. 2023;9(38):eadi9327.
14. Raghu M, Unterthiner T, Kornblith S, Zhang C, Dosovitskiy A. Do vision transformers see like convolutional neural networks? Proceedings of the 35th International Conference on Neural Information Processing Systems; 2021.
15. Chen X, Wang X, Zhou J, Dong C. Activating More Pixels in Image Super-Resolution Transformer. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022:22367–22377.
16. Lau V, Ding Y, Su S, Hu J, Zhang J, Leong ATL, Zhao Y, Wu EX. Ultra-low-field Cardiac Cine MRI with Deep Learning. Paper presented at: Proceedings of the 34th Annual Meeting of ISMRM 2025.
17. Agustsson E, Timofte R. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Paper presented at: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 21–26 July 2017.
18. Timofte R, Agustsson E, Gool LV, Yang MH, Zhang L, Lim B, Son S, Kim H, Nah S, Lee KM, Wang X, Tian Y, Yu K, Zhang Y, Wu S, Dong C, Lin L, Qiao Y, Loy CC, Bae W, Yoo J, Han Y, Ye JC, Choi JS, Kim M, Fan Y, Yu J, Han W, Liu D, Yu H, Wang Z, Shi H, Wang X, Huang TS, Chen Y, Zhang K, Zuo W, Tang Z, Luo L, Li S, Fu M, Cao L, Heng W, Bui G, Le T, Duan Y, Tao D, Wang R, Lin X, Pang J, Xu J, Zhao Y, Xu X, Pan J, Sun D, Zhang Y, Song X, Dai Y, Qin X, Huynh XP, Guo T, Mousavi HS, Vu TH, Monga V, Cruz C, Egiazarian K, Katkovnik V, Mehta R, Jain AK, Agarwalla A, Praveen CVS, Zhou R, Wen H, Zhu C, Xia Z, Wang Z, Guo Q. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. Paper presented at: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 21–26 July 2017.
19. Petersen SE, Matthews PM, Francis JM, Robson MD, Zemrak F, Boubertakh R, Young AA, Hudson S, Weale P, Garratt S. UK Biobank's cardiovascular magnetic resonance protocol. Journal of cardiovascular magnetic resonance. 2016;18(1):8.
20. Zhao Y, Xiao L, Liu Y, Leong AT, Wu EX. Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding‐free MRI. NMR in Biomedicine. 2024;37(7):e4956.
21. Zhao Y, Xiao L, Hu J, Wu EX. Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction. Magnetic Resonance in Medicine. 2024;92(1):112–127.

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http://echo.ismrm.org/p/ISMRM2026/452-03-003