title: Hybrid MV-kV 3D respiratory motion tracking during radiation therapy with low imaging dose.
abstract: A novel real-time adaptive MV-kV imaging framework for image-guided radiation therapy is developed to reduce the thoracic and abdominal tumor targeting uncertainty caused by respiration-induced intrafraction motion with ultra-low patient imaging dose. In our method, continuous stereoscopic MV-kV imaging is used at the beginning of a radiation therapy delivery for several seconds to measure the implanted marker positions. After this stereoscopic imaging period, the kV imager is switched off except for the times when no fiducial marker is detected in the cine-MV images. The 3D time-varying marker positions are estimated by combining the MV 2D projection data and the motion correlations between directional components of marker motion established from the stereoscopic imaging period and updated afterwards; in particular, the most likely position is assumed to be the position on the projection line that has the shortest distance to the first principal component line segment constructed from previous trajectory points. An adaptive windowed auto-regressive prediction is utilized to predict the marker position a short time later (310 ms and 460 ms in this study) to allow for tracking system latency. To demonstrate the feasibility and evaluate the accuracy of the proposed method, computer simulations were performed for both arc and fixed-gantry deliveries using 66 h of retrospective tumor motion data from 42 patients treated for thoracic or abdominal cancers. The simulations reveal that using our hybrid approach, a smaller than 1.2 mm or 1.5 mm root-mean-square tracking error can be achieved at a system latency of 310 ms or 460 ms, respectively. Because the kV imaging is only used for a short period of time in our method, extra patient imaging dose can be reduced by an order of magnitude compared to continuous MV-kV imaging, while the clinical tumor targeting accuracy for thoracic or abdominal cancers is maintained. Furthermore, no additional hardware is required with the proposed method.
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing, People's Republic of China.
authors.forename: Huagang
authors.initials: H
authors.lastname: Yan
authors.forename: Haiyun
authors.initials: H
authors.lastname: Li
authors.forename: Zhixiang
authors.initials: Z
authors.lastname: Liu
authors.forename: Ravinder
authors.initials: R
authors.lastname: Nath
authors.forename: Wu
authors.initials: W
authors.lastname: Liu
mesh_terms: D003198:Computer Simulation; D005240:Feasibility Studies; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000503:physiopathology / Q000532:radiotherapy; D009068:Movement; D025341:Principal Component Analysis; D011829:Radiation Dosage; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D012119:Respiration; D013997:Time Factors