Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
302-02-006 ISMRM Abstract

RadiolGAN: A Multicenter Study of Synthetic CT from 3D UTE MRI to Enhance Pulmonary Radiologic Sign Visualization

Primary: Body - Lung
Secondary: Analysis Methods - Image Synthesis and Translation
302-02-006 · Take a Breath: Chest, Thoracic, and Pulmonary MRI · Monday, 11 May, 8:20 AM–10:10 AM · Hall 1B
Keywords: Synthetic CT Generative adversarial network Image synthesis 3D Ultrashort Echo Time MRI Lung imaging
Accepted
xiaoqing Wu 1, Xi Zhu2, Jie Shi3,4, Chaoyin Tang5, Yanbin Cui6, Jing Ye7, Wennuo Huang7, Wei Xia7
1College of Medical Imaging, Dalian Medical University, Dalian, China
2Department of Radiology, Northern Jiangsu People’s Hospital, Yangzhou, China
3MR Research, Beijing, China
4MR Research, GE HealthCare, shanghai, China
5College of Automation Engineering, nanjing, China
6College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, nanjing, China
7Northern Jiangsu People’s Hospital, Yangzhou, China
Presenting Author: xiaoqing Wu

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References

1. Bae K, Jeon KN, Hwang MJ, et al. Comparison of lung imaging using three-dimensional ultrashort echo time and zero echo time sequences: preliminary study. Eur Radiol. 2019;29(5):2253-2262. doi:10.1007/s00330-018-5889-x [doi]
2. Ohno Y, Koyama H, Yoshikawa T, et al. Pulmonary high-resolution ultrashort TE MR imaging: Comparison with thin-section standard- and low-dose computed tomography for the assessment of pulmonary parenchyma diseases. J Magn Reson Imaging. 2016;43(2):512-532. doi:10.1002/jmri.25008 [doi]
3. Salehjahromi M, Karpinets TV, Sujit SJ, et al. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Cell Rep Med. 2024;5(3):101463. doi:10.1016/j.xcrm.2024.101463 [doi]
4. Longuefosse A, Raoult J, Benlala I, et al. Generating High-Resolution Synthetic CT from Lung MRI with Ultrashort Echo Times: Initial Evaluation in Cystic Fibrosis. Radiology. 2023;308(1):e230052. doi:10.1148/radiol.230052 [doi]
5. D. Ouyang et al., "Efficient Multi-Scale Attention Module with Cross-Spatial Learning," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096516. [doi]
6. M. Zhao, S. Zhong, X. Fu, B. Tang and M. Pecht, "Deep Residual Shrinkage Networks for Fault Diagnosis," in IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4681-4690, July 2020, doi: 10.1109/TII.2019.2943898. [doi]
7. Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization[J]. arXiv. 2017. Doi: https://doi.org/10.48550/arXiv.1703.06868 [doi]

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http://echo.ismrm.org/p/ISMRM2026/302-02-006