{"doi":"10.1109/tim.2023.3318712","title":"SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data","abstract":"This work aims to improve limited-angle (LA) cone beam computed tomography (CBCT) by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.","journal":"IEEE Transactions on Instrumentation and Measurement","year":2023,"id":340268,"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":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9492,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":650309,"name":"Yikun Zhang","orcid":"0000-0002-4048-4869","position":1,"is_corresponding":false},{"id":1074637,"name":"Wangyao Li","orcid":"0000-0002-4480-5455","position":2,"is_corresponding":false},{"id":1074638,"name":"Weijie Zhang","orcid":"0000-0003-4528-234X","position":3,"is_corresponding":false},{"id":1074639,"name":"K. Sripal Reddy","orcid":"0000-0001-5193-5437","position":4,"is_corresponding":false},{"id":1074640,"name":"Qiaoqiao Ding","orcid":"0000-0003-0110-8681","position":5,"is_corresponding":false},{"id":1074641,"name":"Xiaoqun Zhang","orcid":"0000-0002-6583-8766","position":6,"is_corresponding":false},{"id":650310,"name":"Yang Chen","orcid":"0000-0002-5660-6349","position":7,"is_corresponding":false},{"id":1074642,"name":"Hao Gao","orcid":"0000-0002-4253-7418","position":8,"is_corresponding":false},{"id":621642,"name":"Dianlin Hu","orcid":"0000-0003-4857-9878","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-19T01:10:54.145367Z","pmid":"38957474","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":[]}