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Title: SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

Abstract

Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing newmore » features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.« less

Authors:
; ;  [1];  [2];  [3]
  1. Massachusetts General Hospital, Boston, MA (United States)
  2. Stanford Linear Accelerator Center, Menlo Park, CA (United States)
  3. UC San Francisco, San Francisco, CA (United States)
Publication Date:
OSTI Identifier:
22642380
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BRAGG CURVE; COMPUTER CODES; COMPUTERIZED SIMULATION; EXPERIMENTAL DATA; MONTE CARLO METHOD; PROTON BEAMS; QUALITY CONTROL; RADIOTHERAPY; STOCHASTIC PROCESSES

Citation Formats

Hall, D, Schuemann, J, Paganetti, H, Perl, J, and Faddegon, B. SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy. United States: N. p., 2016. Web. doi:10.1118/1.4956275.
Hall, D, Schuemann, J, Paganetti, H, Perl, J, & Faddegon, B. SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy. United States. doi:10.1118/1.4956275.
Hall, D, Schuemann, J, Paganetti, H, Perl, J, and Faddegon, B. Wed . "SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy". United States. doi:10.1118/1.4956275.
@article{osti_22642380,
title = {SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy},
author = {Hall, D and Schuemann, J and Paganetti, H and Perl, J and Faddegon, B},
abstractNote = {Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.},
doi = {10.1118/1.4956275},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: This study provides an overview of the design and commissioning of the Monte Carlo (MC) model of the spot-scanning proton therapy nozzle and its implementation for the patient plan simulation. Methods: The Hitachi PROBEAT V scanning nozzle was simulated based on vendor specifications using the TOPAS extension of Geant4 code. FLUKA MC simulation was also utilized to provide supporting data for the main simulation. Validation of the MC model was performed using vendor provided data and measurements collected during acceptance/commissioning of the proton therapy machine. Actual patient plans using CT based treatment geometry were simulated and compared to themore » dose distributions produced by the treatment planning system (Varian Eclipse 13.6), and patient quality assurance measurements. In-house MATLAB scripts are used for converting DICOM data into TOPAS input files. Results: Comparison analysis of integrated depth doses (IDDs), therapeutic ranges (R90), and spot shape/sizes at different distances from the isocenter, indicate good agreement between MC and measurements. R90 agreement is within 0.15 mm across all energy tunes. IDDs and spot shapes/sizes differences are within statistical error of simulation (less than 1.5%). The MC simulated data, validated with physical measurements, were used for the commissioning of the treatment planning system. Patient geometry simulations were conducted based on the Eclipse produced DICOM plans. Conclusion: The treatment nozzle and standard option beam model were implemented in the TOPAS framework to simulate a highly conformal discrete spot-scanning proton beam system.« less
  • Purpose: To estimate the dose delivered to a moving lung tumor by proton therapy beams of different modulation types, and compare with Monte Carlo predictions. Methods: A radiology support devices (RSD) phantom was irradiated with therapeutic proton radiation beams using two different types of modulation: uniform scanning (US) and double scattered (DS). The Eclipse© dose plan was designed to deliver 1.00Gy to the isocenter of a static ∼3×3×3cm (27cc) tumor in the phantom with 100% coverage. The peak to peak amplitude of tumor motion varied from 0.0 to 2.5cm. The radiation dose was measured with an ion-chamber (CC-13) located withinmore » the tumor. The time required to deliver the radiation dose varied from an average of 65s for the DS beams to an average of 95s for the US beams. Results: The amount of radiation dose varied from 100% (both US and DS) to the static tumor down to approximately 92% for the moving tumor. The ratio of US dose to DS dose ranged from approximately 1.01 for the static tumor, down to 0.99 for the 2.5cm moving tumor. A Monte Carlo simulation using TOPAS included a lung tumor with 4.0cm of peak to peak motion. In this simulation, the dose received by the tumor varied by ∼40% as the period of this motion varied from 1s to 4s. Conclusion: The radiation dose deposited to a moving tumor was less than for a static tumor, as expected. At large (2.5cm) amplitudes, the DS proton beams gave a dose closer to the desired dose than the US beams, but equal within experimental uncertainty. TOPAS Monte Carlo simulation can give insight into the moving tumor — dose relationship. This work was supported in part by the Philips corporation.« less
  • Purpose: Measure stray radiation inside a passive scattering proton therapy facility, compare values to Monte Carlo (MC) simulations and identify the actual needs and challenges. Methods: Measurements and MC simulations were considered to acknowledge neutron exposure associated with 75 MeV ocular or 180 MeV intracranial passively scattered proton treatments. First, using a specifically-designed high sensitivity Bonner Sphere system, neutron spectra were measured at different positions inside the treatment rooms. Next, measurement-based mapping of neutron ambient dose equivalent was fulfilled using several TEPCs and rem-meters. Finally, photon and neutron organ doses were measured using TLDs, RPLs and PADCs set inside anthropomorphicmore » phantoms (Rando, 1 and 5-years-old CIRS). All measurements were also simulated with MCNPX to investigate the efficiency of MC models in predicting stray neutrons considering different nuclear cross sections and models. Results: Knowledge of the neutron fluence and energy distribution inside a proton therapy room is critical for stray radiation dosimetry. However, as spectrometry unfolding is initiated using a MC guess spectrum and suffers from algorithmic limits a 20% spectrometry uncertainty is expected. H*(10) mapping with TEPCs and rem-meters showed a good agreement between the detectors. Differences within measurement uncertainty (10–15%) were observed and are inherent to the energy, fluence and directional response of each detector. For a typical ocular and intracranial treatment respectively, neutron doses outside the clinical target volume of 0.4 and 11 mGy were measured inside the Rando phantom. Photon doses were 2–10 times lower depending on organs position. High uncertainties (40%) are inherent to TLDs and PADCs measurements due to the need for neutron spectra at detector position. Finally, stray neutrons prediction with MC simulations proved to be extremely dependent on proton beam energy and the used nuclear models and cross sections. Conclusion: This work highlights measurement and simulation limits for ion therapy radiation protection applications.« less
  • Purpose: To develop and verify an extension to TOPAS for calculation of dose response models (TCP/NTCP). TOPAS wraps and extends Geant4. Methods: The TOPAS DICOM interface was extended to include structure contours, for subsequent calculation of DVH’s and TCP/NTCP. The following dose response models were implemented: Lyman-Kutcher-Burman (LKB), critical element (CE), population based critical volume (CV), parallel-serials, a sigmoid-based model of Niemierko for NTCP and TCP, and a Poisson-based model for TCP. For verification, results for the parallel-serial and Poisson models, with 6 MV x-ray dose distributions calculated with TOPAS and Pinnacle v9.2, were compared to data from the benchmarkmore » configuration of the AAPM Task Group 166 (TG166). We provide a benchmark configuration suitable for proton therapy along with results for the implementation of the Niemierko, CV and CE models. Results: The maximum difference in DVH calculated with Pinnacle and TOPAS was 2%. Differences between TG166 data and Monte Carlo calculations of up to 4.2%±6.1% were found for the parallel-serial model and up to 1.0%±0.7% for the Poisson model (including the uncertainty due to lack of knowledge of the point spacing in TG166). For CE, CV and Niemierko models, the discrepancies between the Pinnacle and TOPAS results are 74.5%, 34.8% and 52.1% when using 29.7 cGy point spacing, the differences being highly sensitive to dose spacing. On the other hand, with our proposed benchmark configuration, the largest differences were 12.05%±0.38%, 3.74%±1.6%, 1.57%±4.9% and 1.97%±4.6% for the CE, CV, Niemierko and LKB models, respectively. Conclusion: Several dose response models were successfully implemented with the extension module. Reference data was calculated for future benchmarking. Dose response calculated for the different models varied much more widely for the TG166 benchmark than for the proposed benchmark, which had much lower sensitivity to the choice of DVH dose points. This work was supported by National Cancer Institute Grant R01CA140735.« less
  • Purpose: In proton therapy, the relative biological effectiveness (RBE) – compared with conventional photon therapy – is routinely set to 1.1. However, experimental in vitro studies indicate evidence for the variability of the RBE. To clarify the impact on patient treatment, investigation of the RBE in a preclinical case study should be performed. Methods: The Monte Carlo software TOPAS was used to simulate the radiation field of an irradiation setup at the experimental beamline of the proton therapy facility (OncoRay) in Dresden, Germany. Simulations were performed on cone beam CT-data (CBCT) of a xenogeneous mouse with an orthotopic lung carcinomamore » obtained by an in-house developed small animal image-guided radiotherapy device. A homogeneous physical fraction dose of 1.8Gy was prescribed for the contoured tumor volume. Simulated dose and linear energy transfer distributions were used to estimate RBE values in the mouse based on an RBE model by Wedenberg et al. To characterize radiation sensitivity of normal and tumor tissue, α/β-ratios were taken from the literature for NB1RGB (10.1Gy) and human squamous lung cancer (6.2Gy) cell lines, respectively. Results: Good dose coverage of the target volume was achieved with a spread-out Bragg peak (SOBP). The contra-lateral lung was completely spared from receiving radiation. An increase in RBE towards the distal end of the SOBP from 1.07 to 1.35 and from 1.05 to 1.3 was observed when considering normal tissue and tumor, respectively, with the highest RBE values located distal to the target volume. Conclusion: Modeled RBE values simulated on CBCT for experimental preclinical proton therapy varied with tissue type and depth in a mouse and differed therefore from a constant value of 1.1. Further translational work will include, first, conducting preclinical experiments and, second, analogous RBE studies in patients using experimentally verified simulation settings for our clinically used patient-specific beam conforming technique.« less