Automation of Monte Carlo-based treatment plan verification for proton therapy

被引:8
作者
Kaluarachchi, Maduka [1 ]
Moskvin, Vadim [1 ]
Pirlepesov, Fakhriddin [1 ]
Wilson, Lydia J. [1 ]
Xie, Fang [1 ]
Faught, Austin M. [1 ]
机构
[1] St Jude Childrens Res Hosp, Dept Radiat Oncol, 332 N Lauderdale St, Memphis, TN 38105 USA
来源
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS | 2020年 / 21卷 / 08期
关键词
automation; Monte Carlo; proton therapy; BRAIN-STEM; BRAGG PEAK; SIMULATION; HETEROGENEITIES; PARAMETERS; TOPAS;
D O I
10.1002/acm2.12923
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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.
引用
收藏
页码:131 / 138
页数:8
相关论文
共 50 条
  • [1] Monte Carlo-based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy
    Li, Y.
    Tian, Z.
    Song, T.
    Wu, Z.
    Liu, Y.
    Jiang, S. B.
    Jia, X.
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, 2015, VOLS 1 AND 2, 2015, 51 : 475 - 478
  • [2] Monte carlo calculations supporting Patient Plan Verification in Proton Therapy
    Lima, Thiago V. M.
    Dosanjh, Manjit
    Ferrari, Alfredo
    Molineli, Silvia
    Ciocca, Mario
    Mairani, Andrea
    FRONTIERS IN ONCOLOGY, 2016, 6
  • [3] A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy
    Li, Yongbao
    Tian, Zhen
    Shi, Feng
    Song, Ting
    Wu, Zhaoxia
    Liu, Yaqiang
    Jiang, Steve
    Jia, Xun
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (07) : 2903 - 2919
  • [4] Proton Therapy Treatment Plan Verification in CCB Krakow Using Fred Monte Carlo TPS Tool
    Garbacz, M.
    Battistoni, G.
    Durante, M.
    Gajewski, J.
    Krah, N.
    Patera, V.
    Rinaldi, I.
    Schiavi, A.
    Scifoni, E.
    Skrzypek, A.
    Tommasino, F.
    Rucinski, A.
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1, 2019, 68 (01): : 783 - 787
  • [5] A plan verification platform for online adaptive proton therapy using deep learning-based Monte-Carlo denoising
    Zhang, Guoliang
    Chen, Xinyuan
    Dai, Jianrong
    Men, Kuo
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2022, 103 : 18 - 25
  • [6] Monte Carlo framework for commissioning a synchrotron-based discrete spot scanning proton beam system and treatment plan verification
    Moskvin, Vadim P.
    Faught, Austin
    Pirlepesov, Fakhriddin
    Zhao, Li
    Hua, Chia-Ho
    Merchant, Thomas E.
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2021, 7 (04)
  • [7] A Monte Carlo-based treatment-planning tool for ion beam therapy
    Boehlen, T. T.
    Bauer, J.
    Dosanjh, M.
    Ferrari, A.
    Haberer, T.
    Parodi, K.
    Patera, V.
    Mairani, A.
    JOURNAL OF RADIATION RESEARCH, 2013, 54 : 77 - 81
  • [8] A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy
    Li, Yongbao
    Tian, Zhen
    Song, Ting
    Wu, Zhaoxia
    Liu, Yaqiang
    Jiang, Steve
    Jia, Xun
    PHYSICS IN MEDICINE AND BIOLOGY, 2017, 62 (01) : 289 - 305
  • [9] A GPU-Accelerated and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization System
    Ma, J.
    Tseung, H. Wan Chan
    Beltran, C.
    MEDICAL PHYSICS, 2014, 41 (06) : 535 - 536
  • [10] Proton and helium ion radiotherapy for meningioma tumors: a Monte Carlo-based treatment planning comparison
    Thomas Tessonnier
    Andrea Mairani
    Wenjing Chen
    Paola Sala
    Francesco Cerutti
    Alfredo Ferrari
    Thomas Haberer
    Jürgen Debus
    Katia Parodi
    Radiation Oncology, 13