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
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
  • [21] A Monte Carlo Based Proton Therapy Dose Distribution Verification System
    Beltran, C.
    Jia, Y.
    Slopsema, R.
    Yeung, D.
    Li, Z.
    MEDICAL PHYSICS, 2010, 37 (06)
  • [22] A Fully Monte Carlo-Based Efficient Biological Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy
    Qin, N.
    Shen, C.
    Pinto, M.
    Tian, Z.
    Dedes, G.
    Pompos, A.
    Jiang, S.
    Parodi, K.
    Jia, X.
    MEDICAL PHYSICS, 2017, 44 (06) : 3276 - 3276
  • [23] 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
  • [24] DEVELOPMENT OF A MONTE CARLO-BASED TREATMENT PLANNING SYSTEM FOR MICROBEAM RADIATION THERAPY
    Martinez-Rovira, I.
    Sempau, J.
    Bravin, A.
    Alonso, Y. Prezado
    RADIOTHERAPY AND ONCOLOGY, 2010, 96 : S484 - S485
  • [25] TOMOMC, A Monte Carlo-Based Planning Verification Tool for Helical Tomotherapy
    Chibani, O.
    Ma, C.
    MEDICAL PHYSICS, 2015, 42 (06) : 3512 - 3512
  • [26] GPU-accelerated Monte Carlo-based online adaptive proton therapy: A feasibility study
    Feng, Hongying
    Patel, Samir H.
    Wong, William W.
    Younkin, James E.
    Penoncello, Gregory P.
    Morales, Danairis Hernandez
    Stoker, Joshua B.
    Robertson, Daniel G.
    Fatyga, Mirek
    Bues, Martin
    Schild, Steven E.
    Foote, Robert L.
    Liu, Wei
    MEDICAL PHYSICS, 2022, 49 (06) : 3550 - 3563
  • [27] Clinical Evaluations of Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Radiotherapy
    Chi, Y.
    Li, Y.
    Tian, Z.
    Gu, X.
    Jiang, S.
    Jia, X.
    MEDICAL PHYSICS, 2015, 42 (06) : 3372 - 3372
  • [28] A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system
    Ma, Jiasen
    Beltran, Chris
    Tseung, Hok Seum Wan Chan
    Herman, Michael G.
    MEDICAL PHYSICS, 2014, 41 (12)
  • [29] Evaluation of a Novel Monte Carlo Based Plan Validation Software for Proton Therapy
    Spano, J.
    Kassaee, A.
    Bollinger, D.
    MEDICAL PHYSICS, 2022, 49 (06) : E870 - E871
  • [30] Monte Carlo-based independent dose verification of radiosurgery HyperArc plans
    Calvo-Ortega, Juan-Francisco
    Moragues-Femenia, Sandra
    Laosa-Bello, Coral
    Hermida-Lopez, Marcelino
    Pozo-Masso, Miguel
    Zamora-Perez, Antonia
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2022, 102 : 19 - 26