The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery. Issue 11 (14th October 2015)

• Record Type: Journal Article
• Authors: Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke
• Is Part Of: Medical physics. Volume 42:Issue 11(2015)
• Page Start: 6457
• Page End: 6467
• Publication Date: 2015-10-14
• Subjects: phantoms -- quality assurance -- radiation therapy; Quality assurance in radiotherapy -- Simulation -- Design; Radiation therapy; collision avoidance -- radiotherapy delivery automation -- noncoplanar radiotherapy; Collision theories -- Lungs -- Dosimetry -- Linear accelerators -- Image scanners -- Brain -- Medical radiation safety -- Surface collisions; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4932631
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Significant dosimetric benefits had been previously demonstrated in highly noncoplanar treatment plans. In this study, the authors developed and verified an individualized collision model for the purpose of delivering highly noncoplanar radiotherapy and tested the feasibility of total delivery automation with Varian TrueBeam developer mode. Methods: A hand‐held 3D scanner was used to capture the surfaces of an anthropomorphic phantom and a human subject, which were positioned with a computer‐aided design model of a TrueBeam machine to create a detailed virtual geometrical collision model. The collision model included gantry, collimator, and couch motion degrees of freedom. The accuracy of the 3D scanner was validated by scanning a rigid cubical phantom with known dimensions. The collision model was then validated by generating 300 linear accelerator orientations corresponding to 300 gantry‐to‐couch and gantry‐to‐phantom distances, and comparing the corresponding distance measurements to their corresponding models. The linear accelerator orientations reflected uniformly sampled noncoplanar beam angles to the head, lung, and prostate. The distance discrepancies between measurements on the physical and virtual systems were used to estimate treatment‐site‐specific safety buffer distances with 0.1%, 0.01%, and 0.001% probability of collision between the gantry and couch or phantom. Plans containing 20 noncoplanar beams to the brain, lung, and prostate optimized via an in‐house noncoplanar radiotherapy platform were converted into XML script for automated delivery and the entire delivery was recorded and timed to demonstrate the feasibility of automated delivery. Results: The 3D scanner measured the dimension of the 14 cm cubic phantom within 0.5 mm. The maximal absolute discrepancy between machine and model measurements for gantry‐to‐couch and gantry‐to‐phantom was 0.95 and 2.97 cm, respectively. The reduced accuracy of gantry‐to‐phantom measurements was attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site‐specific safety buffer distance with 0.001% probability of collision for (gantry‐to‐couch, gantry‐to‐phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.