W Cheon, Y Han* , W Kim , B Min* , Chungbuk National University HospitalSeoul W Cheon¹*, Y Han^1,2,3 , W Kim⁴ , B Min ⁴, (1) Dept. of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea, (2) Dept. of Radiation Oncology, Samsung Medical Center, Seoul, 06351, Korea, (3) Samsung Medical Center, Sungkyunkwan University SChool of Medicine, Seoul 06351, Korea, (4) Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, 28644, Korea.

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  W Cheon

(Sunday, 7/14/2019)  

Room: ePoster Forums

Purpose: The purpose of this study was to predict the actual position of MLC position during patient treatment without dry-run.

Methods: Clinac (Varian, CA, USA) was used for generating the machine log-file called the Dynalog. The process of predicting the actual position of the Dynalog file from the RT-plan consists of three folds. First, we implemented an algorithm to calculate the expected position of the Dynalog recorded in 50-ms increments from the control-point of the RT-plan recorded at 2-degree intervals for volumetric arc therapy (VMAT). Second, the deep-learning network trained to predict the actual position of the Dynalog by inputs: the expected position, gantry angle, collimator angle, and neighboring MLCs' expected position with seven time-stamps. Third, we reconstruct the 2D-fluence map using the predicted actual position of MLCs.The deep-learning network based on Long-Short-Term-Memory (LSTM) was trained using Dynalog. The first 70% of the total treatment time was used for training the network, and the remaining 30% treatment time data was used as a test set for evaluating the accuracy of the network. The accuracy of the prediction was evaluated using mean absolute error (MAE), root-mean-square error (RMSE). In addition, the 2D-fluence map of predicted actual position was reconstructed and compared with the true actual position of the Dynalog using gamma analysis.

Results: The MAE, RMSE was 0.412, 0.912 mm between the planned position and the actual position, repectively. The gamma passing rate was 3.12% (SD = 1.13) higher than planned 2D-fluence map at gamma criterion with 1%/2 mm.

Conclusion: In this study, a feasible method of the pretreatment QA without dry-run was suggested by predicting the actual movement of the MLCs from the radiation treatment plan.

Funding Support, Disclosures, and Conflict of Interest: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017R1C1B2011257)

Keywords

Quality Assurance, MLC

Taxonomy

TH- External beam- photons: Quality Assurance - IMRT

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