{"doi":"10.1002/mp.70119","title":"Prior‐adapted progressive time‐resolved CBCT reconstruction using a dynamic reconstruction and motion estimation method","abstract":"BACKGROUND: Cone-beam CT (CBCT) captures on-board volumetric anatomy for image guidance and treatment adaptation in radiotherapy. To compensate for respiration-induced anatomical motion, time-resolved CBCT is highly desired to capture the spatiotemporal anatomical variations but faces challenges in accuracy and efficiency due to substantial optimization needed in image reconstruction and motion modeling. PURPOSE: We proposed a fast time-resolved CBCT reconstruction framework, based on a dynamic reconstruction and motion estimation method with new reconstructions initialized and conditioned on prior reconstructions in an adaptive fashion (DREME-adapt). MATERIALS AND METHODS: DREME-adapt reconstructs a time-resolved CBCT sequence from a fractional standard CBCT scan while simultaneously generating a machine learning-based motion model that allows single-projection-driven intra-treatment CBCT estimation and motion tracking. Via DREME-adapt, a virtual fraction is generated from a pre-treatment 4D-CT set of each patient for a clean, \"cold-start\" reconstruction. For subsequent fractions of the same patient, DREME-adapt uses pre-derived motion models and reference CBCTs as initializations to drive a \"warm-start\" reconstruction, based on a lower-cost refining strategy. Three strategies: DREME-cs which drops the \"warm-start\" component, DREME-adapt-vfx which uses a fixed initialization (virtual fraction's reconstruction results), and DREME-adapt-pro which initialize reconstructions through a progressive daisy chain scheme (virtual fraction for fraction 1, fraction 1 for fraction 2, and so on), were evaluated on a digital phantom study (7 motion/anatomical scenarios) and a patient study (seven patients). RESULTS: DREME-adapt allows fast and accurate time-resolved CBCT reconstruction. For the XCAT simulation study, DREME-adapt-pro achieves image reconstruction relative error of 0.14 ± 0.01 and tumor center-of-mass tracking error of 0.92 ± 0.62 mm (mean ± s.d.), compared to 0.15 ± 0.01 and 1.06 ± 0.73 mm for DREME-adapt-vfx, and 0.18 ± 0.01 and 1.96 ± 1.35 mm for DREME-cs. For the real-time motion inference test dataset of the patient study, DREME-adapt-pro localizes moving lung landmarks to a mean ± s.d. error of 2.21 ± 1.79 mm. In comparison, the corresponding values for DREME-adapt-vfx and DREME-cs were 2.53 ± 1.93 mm and 3.22 ± 2.88 mm, respectively. The DREME-adapt-pro training takes 11 min, only 15% of the original DREME algorithm. CONCLUSIONS: With high efficiency and accuracy, DREME-adapt-pro allows on-board time-resolved CBCT reconstruction and enhances the clinical adoption potential of the DREME framework.","journal":"Medical Physics","year":2025,"id":534837,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9593,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":881958,"name":"Hua‐Chieh Shao","orcid":"0009-0003-7664-7257","position":1,"is_corresponding":false},{"id":473762,"name":"You Zhang","orcid":"0000-0002-8033-2755","position":2,"is_corresponding":false},{"id":963278,"name":"Ruizhi Zuo","orcid":"0000-0002-0464-6234","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T02:51:52.019261Z","pmid":"41001572","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":[]}