{"doi":"10.1002/mp.16112","title":"Sequential monoscopic image‐guided motion compensation in tomotherapy stereotactic body radiotherapy (SBRT) for prostate cancer","abstract":"PURPOSE: To manage intra-fractional motions, recent developments in tomotherapy enable a unique capability of adjusting MLC/jaw to track the moving target based on the intra-fractional motions detected by sequential monoscopic imaging. In this study, we evaluated the effectiveness of motion compensation with a realistic imaging rate for prostate stereotactic body radiotherapy (SBRT). The obtained results will guide optimizing treatment parameters and image-guided radiation therapy (IGRT) in tomotherapy using this approach. METHODS: Ten retrospective prostate cases with actual prostate motion curves previously recorded through the Calypso system were used in this study. Based on the recorded peak-to-peak motion, these cases represented either large (> 5 mm) or median (≤ 5 mm) intra-fractional prostate motions. All the cases were re-planned on tomotherapy using 35 Gy/5 fractions SBRT regimen and three different jaw settings of 1 cm static, 2.5 cm static, and 2.5 cm dynamic jaw. Two motion compensation methods were evaluated: a complete compensation that adjusted the jaw and MLC every 0.1 s (the same rate as the Calypso motion trace), and a realistic compensation that adjusted the jaw and MLC at an average imaging interval of 6 s from sequential monoscopic images. An in-house 4D dose calculation software was then applied to calculate the dosimetric outcomes from the original motion-free plan, the motion-contaminated plan, and the two abovementioned motion-compensated plans. During the process, various imaging rates were also simulated in one case with unusually large motions to quantify the impact of the KV-imaging rate on the effectiveness of motion compensation. RESULTS: The effectiveness of motion compensation was evaluated based on the PTV coverage and OAR sparing. Without any motion-compensation, the PTV coverage (PTV V100%) of patients with large prostate motions decreased remarkably to 55%-82% when planning with the 1 cm jaw but to a less level of 67-94% with the 2.5 cm jaw. In contrast, motion compensation improved the PTV coverage (>92%) when combined with the 2.5 cm jaw, but less effective, around 75%-94%, with the 1 cm jaw. For OAR sparing, the bladder D1cc, bladder D10cc, and rectum D1cc all increased in the motion-contaminated plans. Motion compensation improved OAR sparing to the equivalent level of the original motion-free plans. For patients with median prostate motion, motion-induced degradation in PTV coverage was only observed when planning with the 1 cm jaw. After motion compensation, the PTV coverage improved to better than 94% for all three jaw settings. Additionally, the effectiveness of motion compensation depends on the imaging rate. Motion compensation with a typical rate of two KV images per gantry rotation effectively reduces motion-induced dosimetric uncertainties. However, a higher imaging rate is recommended when planning with a 1 cm jaw for patients with large motions. CONCLUSION: Our results demonstrated that the performance of sequential monoscopic imaging-guided motion compensation on tomotherapy depends on the amplitude of intra-fractional prostate motion, the plan parameter settings, especially jaw setting, gantry rotation, and the imaging rate for motion compensation. Creating a patient-specific imaging guidance protocol is essential to balance the effectiveness of motion compensation and achievable imaging rate for intra-fractional motion tracking.","journal":"Medical Physics","year":2022,"id":288377,"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":0.9565,"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":969606,"name":"Edward Chao","orcid":null,"position":1,"is_corresponding":false},{"id":678960,"name":"Tong Zhu","orcid":"0000-0001-7472-3736","position":2,"is_corresponding":false},{"id":277905,"name":"Andrew Z. Wang","orcid":"0000-0002-9781-4494","position":3,"is_corresponding":false},{"id":492195,"name":"Jun Lian","orcid":"0000-0002-2041-9074","position":4,"is_corresponding":false},{"id":969605,"name":"Lan Lŭ","orcid":null,"position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T00:30:14.968626Z","pmid":"36397645","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":[]}