An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

被引:4
|
作者
Khan, Ahtesham Ullah [1 ]
Simiele, Eric A. [2 ]
Lotey, Rajiv [3 ]
DeWerd, Larry A. [1 ]
Yadav, Poonam [4 ]
机构
[1] Univ Wisconsin Madison, Sch Med & Publ Hlth, Dept Med Phys, A1400 600 Highland Ave, Madison, WI 53792 USA
[2] Rutgers Robert Wood Johnson Med Sch, Rutgers Canc Inst New Jersey, Dept Radiat Oncol, New Brunswick, NJ USA
[3] ViewRay Inc, Oakwood Village, OH USA
[4] Northwestern Univ, Feinberg Sch Med, Northwestern Mem Hosp, Dept Radiat Oncol, 251 E Huron St,Gaiter Pavil LC-100, Chicago, IL 60611 USA
来源
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS | 2023年 / 24卷 / 02期
基金
美国国家科学基金会;
关键词
IMRT QA; log files; Monte Carlo; MR-guided RT (MRgRT); DOSE CALCULATION; ADAPTIVE RADIOTHERAPY; LOG FILES; COMPLEXITY; EFFICIENCY; ENGINE; ERRORS;
D O I
10.1002/acm2.13820
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 To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced. Methods The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units. Results The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work. Conclusions An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.
引用
收藏
页数:12
相关论文
共 25 条
  • [1] Fast online Monte Carlo-based IMRT planning for the MRI linear accelerator
    Bol, G. H.
    Hissoiny, S.
    Lagendijk, J. J. W.
    Raaymakers, B. W.
    PHYSICS IN MEDICINE AND BIOLOGY, 2012, 57 (05): : 1375 - 1385
  • [2] Monte Carlo-based QA for IMRT of head and neck cancers
    Tang, F.
    Sham, J.
    Ma, C-M
    Li, J-S
    FIRST EUROPEAN WORKSHOP ON MONTE CARLO TREATMENT PLANNING, 2007, 74
  • [3] Feasibility of in vivo diffusion weighted imaging on a 0.35 T MRI-guided linear accelerator
    Weygand, J.
    Armstrong, T.
    Bryant, J.
    Andreozzi, J.
    Oraiqat, I. M.
    Liveringhouse, C. L.
    Latifi, K.
    Yamoah, K.
    Costello, J. R.
    Moros, E. G.
    El Naqa, I. M.
    Naghavi, A. O.
    Rosenberg, S. A.
    Redler, G.
    RADIOTHERAPY AND ONCOLOGY, 2023, 182 : S1549 - S1551
  • [4] A GPU-Based Monte Carlo QA Tool for IMRT and VMAT
    Graves, Y.
    Kim, G.
    Folkerts, M.
    Teke, T.
    Popescu, I.
    Cervino, L.
    Tian, Z.
    Jia, X.
    Jiang, S.
    MEDICAL PHYSICS, 2012, 39 (06) : 3957 - 3958
  • [5] A Monte Carlo-based procedure for independent monitor unit calculation in IMRT treatment plans
    Pisaturo, O.
    Moeckli, R.
    Mirimanoff, R-O
    Bochud, F. O.
    PHYSICS IN MEDICINE AND BIOLOGY, 2009, 54 (13): : 4299 - 4310
  • [6] Development and Validation of a 0.35T MR-Guided Linear Accelerator Monte Carlo Model in GEANT4
    Khan, A.
    Simiele, E.
    Yadav, P.
    MEDICAL PHYSICS, 2020, 47 (06) : E741 - E741
  • [7] An Evaluation of Patient Independent Image Distortion in a 0.35T MRI-Guided Radiotherapy System
    Ginn, J.
    Agazaryan, N.
    Cao, M.
    Baharom, U.
    Low, D.
    Yang, Y.
    Gao, Y.
    Hu, P.
    Lee, P.
    Lamb, J.
    MEDICAL PHYSICS, 2017, 44 (06) : 3109 - 3110
  • [8] A Custom Web Application for a GPU-Based Monte Carlo IMRT/VMAT QA Tool
    Folkerts, M.
    Graves, Y.
    Gautier, Q.
    Kim, G.
    Jia, X.
    Jiang, S.
    MEDICAL PHYSICS, 2013, 40 (06)
  • [9] Development and evaluation of a GEANT4-based Monte Carlo Model of a 0.35 T MR-guided radiation therapy (MRgRT) linear accelerator
    Khan, Ahtesham Ullah
    Simiele, Eric A.
    Lotey, Rajiv
    DeWerd, Larry A.
    Yadav, Poonam
    MEDICAL PHYSICS, 2021, 48 (04) : 1967 - 1982
  • [10] Evaluation of MRI-Guided Linear Accelerator Based Stereotactic Radiosurgery for Brain Metastasis
    De Ornelas, M.
    Dogan, N.
    Amestoy, W.
    Guerrero, H.
    Diwani, T.
    Mellon, E.
    MEDICAL PHYSICS, 2021, 48 (06)