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Abstract #2428

Unsupervised deep learning for multi-modal MR image registration with topology-preserving dual consistency constraint

Yu Zhang1, Weijian Huang1, Fei Li1, Qiang He2, Haoyun Liang1, Xin Liu1, Hairong Zheng1, and Shanshan Wang1
1Paul C Lauterbur Research Center, Shenzhen Inst. of Advanced Technology, shenzhen, China, 2United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

Multi-modal magnetic resonance (MR) image registration is essential in the clinic to achieve accurate imaging-based disease diagnosis and treatment planning. Although the existing registration methods have achieved good performance and attracted widespread attention, the image details may be lost after registration. In this study, we propose a multi-modal MR image registration with topology-preserving dual consistency constraint, which achieves the best registration performance with a Dice score of 0.813 in identifying stroke lesions.

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