{"doi":"10.21037/qims-21-1194","title":"Unsupervised computed tomography and cone-beam computed tomography image registration using a dual attention network","abstract":"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.","journal":"Quantitative Imaging in Medicine and Surgery","year":2022,"id":256543,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9576,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":905793,"name":"Hui Yan","orcid":"0000-0003-0175-7884","position":1,"is_corresponding":false},{"id":905794,"name":"Fudong Nian","orcid":"0000-0001-9604-7564","position":2,"is_corresponding":false},{"id":778849,"name":"Ronghu Mao","orcid":null,"position":3,"is_corresponding":false},{"id":905795,"name":"Teng Li","orcid":"0000-0002-9144-4154","position":4,"is_corresponding":false},{"id":905792,"name":"Rui Hu","orcid":"0009-0004-9989-1428","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T00:25:25.728895Z","pmid":"35782273","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}