Unsupervised computed tomography and cone-beam computed tomography image registration using a dual attention network

Quantitative Imaging in Medicine and Surgery

article

Rui Hu¹  Hui Yan²  Fudong Nian³  Ronghu Mao⁴  Teng Li¹ 

[1] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education/School of Artificial Intelligence , Anhui University;Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital , Chinese Academy of Medical Sciences and Peking Union Medical College;School of Advanced Manufacturing Engineering , Hefei University;Radiation Oncology , The Affiliated Cancer Hospital of Zhengzhou University

关键词: Image registration;    computed tomography (CT);    cone-beam computed tomography (CBCT);    image-guided radiotherapy (IGRT);    deep-learning;    neural network;   

DOI  :  10.21037/qims-21-1194

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: The registration of computed tomography (CT) and cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT). However, the large intensity variation between CT and CBCT images limits the registration performance and its clinical application in IGRT. In this study, a learning-based unsupervised approach was developed to address this issue and accurately register CT and CBCT images by predicting the deformation field. Methods: A dual attention module was used to handle the large intensity variation between CT and CBCT images. Specifically, a scale-aware position attention block (SP-BLOCK) and a scale-aware channel attention block (SC-BLOCK) were employed to integrate contextual information from the image space and channel dimensions. The SP-BLOCK enhances the correlation of similar features by weighting and aggregating multi-scale features at different positions, while the SC-BLOCK handles the multiple features of all channels to selectively emphasize dependencies between channel maps. Results: The proposed method was compared with existing mainstream methods on the 4D-LUNG data set. Compared to other mainstream methods, it achieved the highest structural similarity (SSIM) and dice similarity coefficient (DICE) scores of 86.34% and 89.74%, respectively, and the lowest target registration error (TRE) of 2.07 mm. Conclusions: The proposed method can register CT and CBCT images with high accuracy without the needs of manual labeling. It provides an effective way for high-accuracy patient positioning and target localization in IGRT.

【 授权许可】

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