Abstract #4354

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

Synopsis

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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