Abstract #0352

Physics-informed Synthetic Data Learning Boosts Multi-Scenario Fast MRI Reconstruction

Zi Wang¹, Xiaotong Yu¹, Chengyan Wang², Weibo Chen³, Jiazheng Wang³, Ying-Hua Chu⁴, Hongwei Sun⁵, Rushuai Li⁶, Peiyong Li⁷, Fan Yang⁸, Haiwei Han⁸, Taishan Kang⁹, Jianzhong Lin⁹, Chen Yang¹⁰, Shufu Chang¹¹, Zhang Shi¹¹, Sha Hua¹², Yan Li¹³, Juan Hu¹⁴, Liuhong Zhu¹⁰, Jianjun Zhou¹⁰, Meijing Lin¹, Jiefeng Guo¹, Congbo Cai¹, Zhong Chen¹, Di Guo¹⁵, and Xiaobo Qu¹⁶

¹Xiamen University, Xiamen, China, ²Fudan University, Shanghai, China, ³Philips Healthcare, Shanghai, China, ⁴Siemens Healthineers Ltd., Shanghai, China, ⁵United Imaging Research Institute of Intelligent Imaging, Beijing, China, ⁶Nanjing First Hospital, Nanjing, China, ⁷Shandong Aoxin Medical Technology Company, Weifang, China, ⁸The First Affiliated Hospital of Xiamen University, Xiamen, China, ⁹Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China, ¹⁰Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China, ¹¹Zhongshan Hospital, Fudan University, Shanghai, China, ¹²Ruijin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China, ¹³Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, ¹⁴The First Affiliated Hospital of Kunming Medical University, Shanghai, China, ¹⁵Xiamen University of Technology, Xiamen, China, ¹⁶Department of Electronic Science, Xiamen University, Xiamen, China

Synopsis

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, MR Physics Model

Motivation: Deep learning (DL) is powerful for fast MRI reconstruction, but remains largely untapped in multiple clinical imaging scenarios.

Goal(s): To provide a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications.

Approach: In this work, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model.

Results: PISF trained on synthetic data enables high-quality, ultra-fast, and robust MRI reconstruction from different 4contrasts, 5 anatomies, 5 vendors and centers, and 2 pathologies, without further re-training.

Impact: Physics-informed synthetic data learning (DL) provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.

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