Real-time High-quality Multi-parametric 4D-MRI Using Deep Learning-based Motion Estimation from Ultra-undersampled Radial K-space
Haonan Xiao1, Yat Lam Wong1, Wen Li1, Chenyang Liu1, Shaohua Zhi1, Weiwei Liu2, Weihu Wang2, Yibao Zhang2, Hao Wu2, Ho-Fun Victor Lee3, Lai-Yin Andy Cheung4, Hing-Chiu Charles Chang5, Tian Li1, and Jing Cai1
1Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China, 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China, 3Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China, 4Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China, 5Department of Radiology, The University of Hong Kong, Hong Kong, China
We have developed and validated a deep learning-based real-time high-quality (HQ) multi-parametric (Mp) 4D-MRI technique. A dual-supervised downsampling-invariant deformable registration (D3R) model was trained on retrospectively downsampled 4D-MRI with 100 radial spokes in the k-space. The deformations obtained from the downsampled 4D-MRI were applied to 3D-MRI to reconstruct HQ Mp 4D-MRI. The D3R model provides accurate and stable registration performance at up to 500 times downsampling, and the HQ Mp 4D-MRI shows significantly improved quality with sub-voxel level motion accuracy. This technique provides HQ Mp 4D-MRI within 500 ms and holds great potential in online tumor tracking in MR-guided radiotherapy.
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