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Abstract #0862

Ultra-quality 4D-MRI synthesis using deep learning-based deformable image registration

Haonan Xiao1, Tian Li1, Jiang Zhang1, Ruiyan Ni1, Ge Ren1, Yibao Zhang2, Weiwei Liu2, Weihu Wang2, Hao Wu2, Victor Lee3, Andy Cheung3, Hing-Chiu Chang3, and Jing Cai1
1The Hong Kong Polytechnic University, Hong Kong, China, 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China, 3The University of Hong Kong, Hong Kong, China

We have developed and validated an ultra-quality 4D-MRI synthesis technique using deep learning-based deformable image registrations. The displacement vector fields between breathing frames were obtained from low-quality 4D-MRI. They were then applied to high-quality stationary T1, T2, and diffusion weighted images to generate ultra-quality 4D-MRI. The synthetic 4D-MRIs were verified in terms of tumor motion accuracy and image quality. All the motion errors were in a sub-voxel level, and the image quality was significantly improved. This technique holds great potential in volumetric tumor tracking with high accuracy.

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