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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