Automation of Monte Carlo‐based treatment plan verification for proton therapy. Issue 8 (26th May 2020)

• Record Type: Journal Article
• Authors: Kaluarachchi, Maduka; Moskvin, Vadim; Pirlepesov, Fakhriddin; Wilson, Lydia J.; Xie, Fang; Faught, Austin M.
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 8(2020)
• Page Start: 131
• Page End: 138
• Publication Date: 2020-05-26
• Subjects: automation -- Monte Carlo -- proton therapy; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12923
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in‐depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow. Materials and Methods: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high‐performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria. Results: The automation reduced the human‐hours required to initiate and run a calculation to 1–2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm. Conclusion: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment.