title: Comparison of 2D and 3D modeled tumor motion estimation/prediction for dynamic tumor tracking during arc radiotherapy.
abstract: Many real-time imaging techniques have been developed to localize a target in 3D space or in a 2D beam's eye view (BEV) plane for intrafraction motion tracking in radiation therapy. With tracking system latency, the 3D-modeled method is expected to be more accurate even in terms of 2D BEV tracking error. No quantitative analysis, however, has been reported. In this study, we simulated co-planar arc deliveries using respiratory motion data acquired from 42 patients to quantitatively compare the accuracy between 2D BEV and 3D-modeled tracking in arc therapy and to determine whether 3D information is needed for motion tracking. We used our previously developed low kV dose adaptive MV-kV imaging and motion compensation framework as a representative of 3D-modeled methods. It optimizes the balance between additional kV imaging dose and 3D tracking accuracy and solves the MLC blockage issue. With simulated Gaussian marker detection errors (zero mean and 0.39 mm standard deviation) and ~155/310/460 ms tracking system latencies, the mean percentage of time that the target moved  >2 mm from the predicted 2D BEV position are 1.1%/4.0%/7.8% and 1.3%/5.8%/11.6% for the 3D-modeled and 2D-only tracking, respectively. The corresponding average BEV RMS errors are 0.67/0.90/1.13 mm and 0.79/1.10/1.37 mm. Compared to the 2D method, the 3D method reduced the average RMS unresolved motion along the beam direction from ~3 mm to ~1 mm, resulting in on average only  <1% dosimetric advantage in the depth direction. Only for a small fraction of the patients, when tracking latency is long, the 3D-modeled method showed significant improvement of BEV tracking accuracy, indicating potential dosimetric advantage. However, if the tracking latency is short (~150 ms or less), those improvements are limited. Therefore, 2D BEV tracking has sufficient targeting accuracy for most clinical cases. The 3D technique is, however, still important in solving the MLC blockage problem during 2D BEV tracking.
authors.affiliation: Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, United States of America.
authors.forename: Wu
authors.initials: W
authors.lastname: Liu
authors.forename: Xiangyu
authors.initials: X
authors.lastname: Ma
authors.forename: Huagang
authors.initials: H
authors.lastname: Yan
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Ravinder
authors.initials: R
authors.lastname: Nath
authors.forename: Haiyun
authors.initials: H
authors.lastname: Li
mesh_terms: D000008:Abdominal Neoplasms / Q000503:physiopathology* / Q000532:radiotherapy; D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009068:Movement; D011874:Radiometry / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D013899:Thoracic Neoplasms / Q000503:physiopathology* / Q000532:radiotherapy