{"doi":"10.1109/trpms.2021.3133510","title":"Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN)","abstract":"4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images reconstructed by compressed sensing (CS)-based methods suffer from blurred edges. Introducing the average-4D-image constraint to the CS-based reconstruction, such as prior-image-constrained CS (PICCS), can improve the edge sharpness of the stable structures. However, PICCS can lead to motion artifacts in the moving regions. In this study, we proposed a dual-encoder convolutional neural network (DeCNN) to realize the average-image-constrained 4D-CBCT reconstruction. The proposed DeCNN has two parallel encoders to extract features from both the under-sampled target phase images and the average images. The features are then concatenated and fed into the decoder for the high-quality target phase image reconstruction. The reconstructed 4D-CBCT using of the proposed DeCNN from the real lung cancer patient data showed (1) qualitatively, clear and accurate edges for both stable and moving structures; (2) quantitatively, low-intensity errors, high peak signal-to-noise ratio, and high structural similarity compared to the ground truth images; and (3) superior quality to those reconstructed by several other state-of-the-art methods including the back-projection, CS total-variation, PICCS, and the single-encoder CNN. Overall, the proposed DeCNN is effective in exploiting the average-image constraint to improve the 4D-CBCT image quality.","journal":"IEEE Transactions on Radiation and Plasma Medical Sciences","year":2021,"id":176966,"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":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9519,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":504704,"name":"Zeyu Zhang","orcid":"0000-0002-7796-2887","position":1,"is_corresponding":false},{"id":411720,"name":"Yushi Chang","orcid":"0000-0001-6566-0796","position":2,"is_corresponding":false},{"id":720446,"name":"Yun Ge","orcid":"0009-0003-4212-1214","position":3,"is_corresponding":false},{"id":345264,"name":"F Yin","orcid":"0000-0002-2025-4740","position":4,"is_corresponding":false},{"id":364160,"name":"Lei Ren","orcid":"0000-0002-6136-3505","position":5,"is_corresponding":false},{"id":364327,"name":"Zhuoran Jiang","orcid":"0000-0002-7637-1102","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-18T23:47:28.018172Z","pmid":"35386935","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":[]}