CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration. (September 2022)

• Record Type: Journal Article
• Authors: Cao, Yuzhu; Fu, Tianxiao; Duan, Luwen; Dai, Yakang; Gong, Lun; Cao, Weiwei; Liu, Desen; Yang, Xiaodong; Ni, Xinye; Zheng, Jian
• Is Part Of: Computer methods and programs in biomedicine. Volume 224(2022)
• Publication Date: 2022-09
• Subjects: Deformable registration -- Cross-domain fusion network -- Edge-guided attention module -- Cross-domain attention module -- CT-to-CBCT image registration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107025
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel cross-domain fusion registration network for CT-to-CBCT image registration. The network can capture edge information from gradient images and model the spatial correspondence between two image domains. Cross-domain fusion can improve the performance of registration. Abstract: Background and Objective: Computer tomography (CT) to cone-beam computed tomography (CBCT) image registration plays an important role in radiotherapy treatment placement, dose verification, and anatomic changes monitoring during radiotherapy. However, fast and accurate CT-to-CBCT image registration is still very challenging due to the intensity differences, the poor image quality of CBCT images, and inconsistent structure information. Methods: To address these problems, a novel unsupervised network named cross-domain fusion registration network (CDFRegNet) is proposed. First, a novel edge-guided attention module (EGAM) is designed, aiming at capturing edge information based on the gradient prior images and guiding the network to model the spatial correspondence between two image domains. Moreover, a novel cross-domain attention module (CDAM) is proposed to improve the network's ability to guide the network to effectively map and fuse the domain-specific features. Results: Extensive experiments on a real clinical dataset were carried out, and the experimental results verify that the proposed CDFRegNet can register CT to CBCT images effectively and obtain the best performance, while compared with other representative methods, with a mean DSC of 80.01±7.16%, a mean TRE of 2.27±0.62 mm, and a mean MHD of 1.50±0.32 mm. The ablation experiments also proved that our EGAM and CDAM can further improve the accuracy of the registration network and they can generalize well to other registration networks. Conclusion: This paper proposed a novel CT-to-CBCT registration method based on EGAM and CDAM, which has the potential to improve the accuracy of multi-domain image registration.