文献类型：期刊文献

英文题名：BCSwinReg: A cross-modal attention network for CBCT-to-CT multimodal image registration

作者：Zhang, Jieming[1];Qing, Chang[1];Li, Yu[1];Wang, Yaqi[1]

机构：[1]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：171

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：;EI(收录号：20240815596737);WOS:【SCI-EXPANDED(收录号:WOS:001200056200001)】；

基金：Funding This work was supported by the National Natural Science Foundation of China No. 61976095.

语种：英文

外文关键词：Cross-domain attention network; Computed tomography; Cone beam computed tomography; Deep learning; Medical image registration

摘要：Computed tomography (CT) and cone beam computed tomography (CBCT) registration plays an important role in radiotherapy. However, the poor quality of CBCT makes CBCT-CT multimodal registration challenging. Effective feature fusion and mapping often lead to better registration results for multimodal registration. Therefore, we proposed a new backbone network BCSwinReg and a cross-modal attention module CrossSwin. Specifically, a cross-modal attention CrossSwin is designed to promote multi-modal feature fusion, map the multi-modal domain to the common domain, and thus helping the network learn the correspondence between images better. Furthermore, a new network, BCSwinReg, is proposed to discover correspondence through crossattention exchange information, obtain multi-level semantic information through a multi-resolution strategy, and finally integrate the deformation of multi-resolutions by the divide-conquer cascade method. We performed experiments on the publicly available 4D-Lung dataset to demonstrate the effectiveness of CrossSwin and BCSwinReg. Compared with VoxelMorph, the BCSwinReg has obtained performance improvements of 3.3% in Dice Similarity Coefficient (DSC) and 0.19 in the average 95% Hausdorff distance (HD95).