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Title:: Research on unsupervised registration method of bladder cancer multi-parameter MRI based on deep learning

Author(s):: CHEN Zhiying; CHEN Chunxiao; WU Zejing; XU Junqi; （Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China）

Keywords:: Diagnosis of bladder cancer; Multi-parameter MRI; Cross-modal registration; Deep learning; End-to-end; Unsupervised learning; Similarity measure

摘要:: 针对传统基于迭代优化的医学影像配准方法速度慢、泛化性差的问题，本研究提出了一种基于深度学习的膀胱癌磁共振成像(magnetic resonance image, MRI)跨模态无监督配准方法，并采用具有随机块采样的标准互信息(patch normalized mutual information, Patch-NMI)进行无监督训练。相比传统的迭代配准方法，本研究算法在进行膀胱癌动态增强成像(dynamic contrast-enhanced imaging, DCE)和T2加权像(T2-weighted imaging, T2WI)配准时，精度提升了1.3%，速度提高了20.42倍。实验结果表明，本算法在进行膀胱癌DCE和T2WI配准时，精度更高，速度更快。

Abstract:: To solve the slow speed and poor generalization of traditional medical image registration based on iterative optimization, we proposed a cross-modal unsupervised registration method of bladder cancer MRI based on deep learning.The patch normalized mutual information (Patch-NMI) loss with random block sampling was used for unsupervised training. Compared with the traditional iterative registration method，the accuracy of the proposed algorithm was improved by 1.3%, and the speed was improved by 20.42 times in the registration of dynamic contrast-enhanced imaging (DCE) and T2-weighted imaging (T2WI). The results show that this algorithm has higher accuracy and faster speed in the registration of DCE and T2WI.