331-02-003 / 331-02-003 ISMRM Abstract

Personalized Specific Absorption Rate Prediction in Ultra-High Field MRI Based on Cycle-Consistent Generative Adversarial Net

Primary: Physics & Engineering - Safety

Secondary: Analysis Methods - Classification and Prediction

331-02-003 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace

Keywords: Deep learning RF Safety Ultra high field MRI 5T MRI B1 inhomogeneity

Accepted

Yizhi Cui ^1,2, Shao Che³, zhuoxv cui^4,5,6, Shahzeb Hayat^1,7, Nan Li^1,7, Enhua Xiao¹, Peiyu He^1,7, Dong Liang^4,6,8,9, Ye Li^5,7,9

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

²Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

³Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China

⁴Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

⁵State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China

⁶University of Chinese Academy of Sciences, Beijing, China

⁷Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, China

⁸State Key Laboratory of Biomedical Imaging Science and System, Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China

⁹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Presenting Author: Yizhi Cui

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References

1. M.-C. Gosselin et al., "Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0," Physics in Medicine & Biology, vol. 59, no. 18, p. 5287, 2014. DOI:10.1088/0031-9155/59/18/5287 [doi]

2. E. Meliadò et al., "A deep learning method for image‐based subject‐specific local SAR assessment," Magnetic resonance in medicine, vol. 83, no. 2, pp. 695-711, 2020. DOI: 10.1002/mrm.27948 [doi]

3. S. Gokyar, C. Zhao, S. J. Ma, and D. J. Wang, "Deep learning‐based local SAR prediction using B 1 maps and structural MRI of the head for parallel transmission at 7 T," Magnetic resonance in medicine, vol. 90, no. 6, pp. 2524-2538, 2023. DOI: 10.1002/mrm.29797 [doi]

4. Che S, Cui Z, Liu J, Ding S, Cao P, Zhang X, Liu X, Zheng H, Liang D, Li Y. Patient-specific Local SAR estimation by combined field mapping and deep-learning method. In: Proceedings of the 2023 ISMRM & ISMRT Annual Meeting & Exhibition, 2023 June 03-08; Toronto, O, Canada. Abstract 2693. DOI: https://doi.org/10.58530/2023/2693 [doi]

5. Che S, Liu J, Cui Z, Ding S, Wang C, Meersman T, Zhang X, Li Y. Transmit uniformity and SAR optimization by a deep-learning method in UHF imaging. In: Proceedings of the 2024 ISMRM & ISMRT Annual Meeting & Exhibition, 2024 May 04-09; Singapore. Abstract 3741. DOI: https://doi.org/10.58530/2024/3741 [doi]

6. Hayat S, Che S, Liu J, Cui Z, Ding S, Wang C, Meersman T, Zhang X, Li Y. Safety-Optimized SAR Prediction for MRI Using Deep Learning Method at 5.0 T. In: Proceedings of the 2025 ISMRM & ISMRT Annual Meeting & Exhibition, 2025 May 10-15; Honolulu, H, USA. Abstract 3405. DOI: https://doi.org/10.58530/2025/3405 [doi]

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http://echo.ismrm.org/p/ISMRM2026/331-02-003