{"doi":"10.1088/1361-6560/abe9f6","title":"An unsupervised 2D–3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation","abstract":"Acquiring CBCTs from a limited scan angle can help to reduce the imaging time, save the imaging dose, and allow continuous target localizations through arc-based treatments with high temporal resolution. However, insufficient scan angle sampling leads to severe distortions and artifacts in the reconstructed CBCT images, limiting their clinical applicability. 2D-3D deformable registration can map a prior fully-sampled CT/CBCT volume to estimate a new CBCT, based on limited-angle on-board cone-beam projections. The resulting CBCT images estimated by 2D-3D deformable registration can successfully suppress the distortions and artifacts, and reflect up-to-date patient anatomy. However, traditional iterative 2D-3D deformable registration algorithm is very computationally expensive and time-consuming, which takes hours to generate a high quality deformation vector field (DVF) and the CBCT. In this work, we developed an unsupervised, end-to-end, 2D-3D deformable registration framework using convolutional neural networks (2D3D-RegNet) to address the speed bottleneck of the conventional iterative 2D-3D deformable registration algorithm. The 2D3D-RegNet was able to solve the DVFs within 5 seconds for 90 orthogonally-arranged projections covering a combined 90° scan angle, with DVF accuracy superior to 3D-3D deformable registration, and on par with the conventional 2D-3D deformable registration algorithm. We also performed a preliminary robustness analysis of 2D3D-RegNet towards projection angular sampling frequency variations, as well as scan angle offsets. The synergy of 2D3D-RegNet with biomechanical modeling was also evaluated, and demonstrated that 2D3D-RegNet can function as a fast DVF solution core for further DVF refinement.","journal":"Physics in Medicine and Biology","year":2021,"id":167741,"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":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9369,"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":695483,"name":"You Zhang","orcid":"0000-0002-4649-278X","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-18T23:45:58.359801Z","pmid":"33631734","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":[]}