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Ortiz R, Sawkey D, Faddegon B, D-Kondo N, Ramos-Méndez J. An interface tool to parametrize treatment plans for the TrueBeam radiotherapy system into TOPAS parameter control files for Monte Carlo simulation. Phys Med 2024; 124:104485. [PMID: 39059251 PMCID: PMC11323898 DOI: 10.1016/j.ejmp.2024.104485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/24/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
PURPOSE The Monte Carlo (MC) method, the gold standard method for radiotherapy dose calculations, is underused in clinical research applications mainly due to computational speed limitations. Another reason is the time-consuming and error prone conversion of treatment plan specifications into MC parameters. To address this issue, we developed an interface tool that creates a set of TOPAS parameter control files (PCF) from information exported from a clinical treatment planning system (TPS) for plans delivered by the TrueBeam radiotherapy system. METHODS The interface allows the user to input DICOM-RT files, exported from a TPS and containing the plan parameters, and choose different multileaf-collimator models, variance reduction technique parameters, scoring quantities and simulation output formats. Radiation sources are precomputed phase space files obtained from Varian. Based on this information, ready-to-run TOPAS PCF that incorporate the position and angular rotation of the TrueBeam dynamic collimation devices, gantry, couch, and patient according to treatment plan specifications are created. RESULTS Dose distributions computed using these PCF were compared against predictions from commercial TPS for different clinical treatment plans and techniques (3D-CRT, IMRT step-and-shoot and VMAT) to evaluate the performance of the interface. The agreement between dose distributions from TOPAS and TPS (>98 % pass ratio in the gamma test) confirmed the correct parametrization of treatment plan specifications into MC PCF. CONCLUSIONS This interface tool is expected to widen the use of MC methods in the clinical medical physics field by facilitating the straightforward transfer of treatment plan parameters from commercial TPS into MC PCF.
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Affiliation(s)
- Ramon Ortiz
- University of California San Francisco, Department of Radiation Oncology 1600 Divisadero Street, San Francisco, CA 94143, United States
| | - Daren Sawkey
- Varian, A Siemens Healthineers Company, 3100 Hansen Way, Palo Alto CA 94034, United States
| | - Bruce Faddegon
- University of California San Francisco, Department of Radiation Oncology 1600 Divisadero Street, San Francisco, CA 94143, United States
| | - Naoki D-Kondo
- University of California San Francisco, Department of Radiation Oncology 1600 Divisadero Street, San Francisco, CA 94143, United States
| | - José Ramos-Méndez
- University of California San Francisco, Department of Radiation Oncology 1600 Divisadero Street, San Francisco, CA 94143, United States.
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Ramos-Méndez J, Park C, Sharma M. Dosimetric characterization of single- and dual-port temporary tissue expanders for postmastectomy radiotherapy using Monte Carlo methods. Front Oncol 2023; 13:1124838. [PMID: 37143943 PMCID: PMC10151677 DOI: 10.3389/fonc.2023.1124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/31/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose The aim of this work was two-fold: a) to assess two treatment planning strategies for accounting CT artifacts introduced by temporary tissue-expanders (TTEs); b) to evaluate the dosimetric impact of two commercially available and one novel TTE. Methods The CT artifacts were managed using two strategies. 1) Identifying the metal in the RayStation treatment planning software (TPS) using image window-level adjustments, delineate a contour enclosing the artifact, and setting the density of the surrounding voxels to unity (RS1). 2) Registering a geometry template with dimensions and materials from the TTEs (RS2). Both strategies were compared for DermaSpan, AlloX2, and AlloX2-Pro TTEs using Collapsed Cone Convolution (CCC) in RayStation TPS, Monte Carlo simulations (MC) using TOPAS, and film measurements. Wax slab phantoms with metallic ports and breast phantoms with TTEs balloons were made and irradiated with a 6 MV AP beam and partial arc, respectively. Dose values along the AP direction calculated with CCC (RS2) and TOPAS (RS1 and RS2) were compared with film measurements. The impact in dose distributions was evaluated with RS2 by comparing TOPAS simulations with and without the metal port. Results For the wax slab phantoms, the dose differences between RS1 and RS2 were 0.5% for DermaSpan and AlloX2 but 3% for AlloX2-Pro. From TOPAS simulations of RS2, the impact in dose distributions caused by the magnet attenuation was (6.4 ± 0.4) %, (4.9 ± 0.7)%, and (2.0 ± 0.9)% for DermaSpan, AlloX2, and AlloX2-Pro, respectively. With breast phantoms, maximum differences in DVH parameters between RS1 and RS2 were as follows. For AlloX2 at the posterior region: (2.1 ± 1.0)%, (1.9 ± 1.0)% and (1.4 ± 1.0)% for D1, D10, and average dose, respectively. For AlloX2-Pro at the anterior region (-1.0 ± 1.0)%, (-0.6 ± 1.0)% and (-0.6 ± 1.0)% for D1, D10 and average dose, respectively. The impact in D10 caused by the magnet was at most (5.5 ± 1.0)% and (-0.8 ± 1.0)% for AlloX2 and AlloX2-Pro, respectively. Conclusion Two strategies for accounting for CT artifacts from three breast TTEs were assessed using CCC, MC, and film measurements. This study showed that the highest differences with respect to measurements occurred with RS1 and can be mitigated if a template with the actual port geometry and materials is used.
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Shan J, Feng H, Morales DH, Patel SH, Wong WW, Fatyga M, Bues M, Schild SE, Foote RL, Liu W. Virtual particle Monte Carlo: A new concept to avoid simulating secondary particles in proton therapy dose calculation. Med Phys 2022; 49:6666-6683. [PMID: 35960865 PMCID: PMC9588716 DOI: 10.1002/mp.15913] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND In proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions. PURPOSE A novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU-accelerated proton MC dose calculation and take full advantage of the computing power of GPU. METHODS Neutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous-slowing-down-approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient-specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared. RESULTS Integrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D-3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D-3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D-3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries. CONCLUSION VPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.
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Affiliation(s)
- Jie Shan
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | | | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Robert L. Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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Park H, Paganetti H, Schuemann J, Jia X, Min CH. Monte Carlo methods for device simulations in radiation therapy. Phys Med Biol 2021; 66:10.1088/1361-6560/ac1d1f. [PMID: 34384063 PMCID: PMC8996747 DOI: 10.1088/1361-6560/ac1d1f] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/12/2021] [Indexed: 11/12/2022]
Abstract
Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.
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Affiliation(s)
- Hyojun Park
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Xun Jia
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75235, United States of America
| | - Chul Hee Min
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
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Park H, Paganetti H, Schuemann J, Jia X, Min CH. Monte Carlo methods for device simulations in radiation therapy. Phys Med Biol 2021. [PMID: 34384063 DOI: 10.1088/1361-6560/ac1d1f.10.1088/1361-6560/ac1d1f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.
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Affiliation(s)
- Hyojun Park
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Xun Jia
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75235, United States of America
| | - Chul Hee Min
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
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Fjæra LF, Indelicato DJ, Stokkevåg CH, Muren LP, Hsi WC, Ytre-Hauge KS. Implementation of a double scattering nozzle for Monte Carlo recalculation of proton plans with variable relative biological effectiveness. Phys Med Biol 2020; 65. [PMID: 33053524 DOI: 10.1088/1361-6560/abc12d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/14/2020] [Indexed: 11/12/2022]
Abstract
A constant relative biological effectiveness (RBE) of 1.1 is currently used in clinical proton therapy. However, theRBEvaries with factors such as dose level, linear energy transfer (LET) and tissue type. MultipleRBEmodels have been developed to account for this biological variation. To enable recalculation of patients treated with double scattering (DS) proton therapy, includingLETand variableRBE, we implemented and commissioned a Monte Carlo (MC) model of a DS treatment nozzle. The main components from the IBA nozzle were implemented in the FLUKA MC code. We calibrated and verified the following entities to experimental measurements: range of pristine Bragg peaks (PBPs) and spread-out Bragg peaks (SOBPs), energy spread, lateral profiles, compensator range degradation, and absolute dose. We recalculated two patients with different field setups, comparing FLUKA vs. treatment planning system (TPS) dose, also obtainingLETand variableRBEdoses. We achieved good agreement between FLUKA and measurements. The range differences between FLUKA and measurements were for the PBPs within ±0.9 mm (83% ⩽ 0.5 mm), and for SOBPs ±1.6 mm (82% ⩽ 0.5 mm). The differences in modulation widths were below 5 mm (79% ⩽ 2 mm). The differences in the distal dose fall off (D80%-D20%) were below 0.5 mm for all PBPs and the lateral penumbras diverged from measurements by less than 1 mm. The mean dose difference (RBE= 1.1) in the target between the TPS and FLUKA were below 0.4% in a three-field plan and below 1.4% in a four-field plan. A dose increase of 9.9% and 7.2% occurred when using variableRBEfor the two patients, respectively. We presented a method to recalculate DS proton plans in the FLUKA MC code. The implementation was used to obtainLETand variableRBEdose and can be used for investigating variableRBEfor previously treated patients.
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Affiliation(s)
- Lars Fredrik Fjæra
- Department of Physics and Technology, University of Bergen, Bergen, Norway
| | - Daniel J Indelicato
- Department of Radiation Oncology, University of Florida, Jacksonville, FL, United States of America
| | - Camilla H Stokkevåg
- Department of Physics and Technology, University of Bergen, Bergen, Norway.,Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Ludvig P Muren
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Wen C Hsi
- Department of Radiation Oncology, University of Florida, Jacksonville, FL, United States of America
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Faddegon B, Ramos-Méndez J, Schuemann J, McNamara A, Shin J, Perl J, Paganetti H. The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research. Phys Med 2020; 72:114-121. [PMID: 32247964 DOI: 10.1016/j.ejmp.2020.03.019] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 02/06/2020] [Accepted: 03/19/2020] [Indexed: 01/02/2023] Open
Abstract
PURPOSE This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS. MATERIALS AND METHODS Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4. RESULTS After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool. CONCLUSIONS The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.
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Affiliation(s)
- Bruce Faddegon
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
| | - José Ramos-Méndez
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Jan Schuemann
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Aimee McNamara
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Jungwook Shin
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Joseph Perl
- SLAC National Accelerator Laboratory, Menlo Park, USA
| | - Harald Paganetti
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
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Eulitz J, Lutz B, Wohlfahrt P, Dutz A, Enghardt W, Karpowitz C, Krause M, Troost EGC, Lühr A. A Monte Carlo based radiation response modelling framework to assess variability of clinical RBE in proton therapy. Phys Med Biol 2019; 64:225020. [PMID: 31374558 DOI: 10.1088/1361-6560/ab3841] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The clinical implementation of a variable relative biological effectiveness (RBE) in proton therapy is currently controversially discussed. Initial clinical evidence indicates a variable proton RBE, which needs to be verified. In this study, a radiation response modelling framework for assessing clinical RBE variability is established. It was applied to four selected glioma patients (grade III) treated with adjuvant radio(chemo)therapy and who developed late morphological image changes on T1-weighted contrast-enhanced (T1w-CE) magnetic resonance (MR) images within approximately two years of recurrence-free follow-up. The image changes were correlated voxelwise with dose and linear energy transfer (LET) values using univariable and multivariable logistic regression analysis. The regression models were evaluated by the area-under-the-curve (AUC) method performing a leave-one-out cross validation. The tolerance dose TD50 at which 50% of patient voxels experienced toxicity was interpolated from the models. A Monte Carlo (MC) model was developed to simulate dose and LET distributions, which includes variance reduction (VR) techniques to decrease computation time. Its reliability and accuracy were evaluated based on dose calculations of the clinical treatment planning system (TPS) as well as absolute dose measurements performed in the patient specific quality assurance. Morphological image changes were related to a combination of dose and LET. The multivariable models revealed cross-validated AUC values of up to 0.88. The interpolated TD50 curves decreased with increasing LET indicating an increase in biological effectiveness. The MC model reliably predicted average TPS dose within the clinical target volume as well as absolute water phantom dose measurements within 2% accuracy using dedicated VR settings. The observed correlation of dose and LET with late brain tissue damage suggests considering RBE variability for predicting chronic radiation-induced brain toxicities. The MC model simulates radiation fields in patients precisely and time-efficiently. Hence, this study encourages and enables in-depth patient evaluation to assess the variability of clinical proton RBE.
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Affiliation(s)
- J Eulitz
- Faculty of Medicine and University Hospital Carl Gustav Carus, OncoRay-National Center for Radiation Research in Oncology, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. Faculty of Medicine and University Hospital Carl Gustav Carus, Department of Radiotherapy and Radiation Oncology, Technische Universität Dresden, Dresden, Germany. Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
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Liu H, Li Z, Slopsema R, Hong L, Pei X, Xu XG. TOPAS Monte Carlo simulation for double scattering proton therapy and dosimetric evaluation. Phys Med 2019; 62:53-62. [PMID: 31153399 DOI: 10.1016/j.ejmp.2019.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/15/2019] [Accepted: 05/01/2019] [Indexed: 10/26/2022] Open
Abstract
PURPOSE To construct and commission a double scattering (DS) proton beam model in TOPAS Monte Carlo (MC) code. Dose comparisons of MC calculations to the measured and treatment planning system (TPS) calculated dose were performed. METHODS The TOPAS nozzle model was based on the manufacturer blueprints. Nozzle set-up and beam current modulations were calculated using room-specific calibration data. This model was implemented to reproduce pristine peaks, spread-out Bragg peaks (SOBP) and lateral profiles. A stair-shaped target plan in water phantom was calculated and compared to measured data to verify range compensator (RC) modeling. RESULTS TOPAS calculated pristine peaks agreed well with measurements, with accuracies of 0.03 cm for range R90 and 0.05 cm for distal dose fall-off (DDF). The calculated SOBP range, modulation width and DDF differences between MC calculations and measurements were within 0.05 cm, 0.5 cm and 0.03 cm respectively. MC calculated lateral penumbra agreed well with measured data, with difference less than 0.05 cm. For RC calculation, TPS underestimated the additional depth dose tail due to the nuclear halo effect. Lateral doses by TPS were 10% lower than measurement outside the target, while maximum difference of MC calculation was within 2%. At deeper depths inside the target volume, TPS overestimated doses by up to 25% while TOPAS predicted the dose to within 5% of measurements. CONCLUSION We have successfully developed and commissioned a MC based DS nozzle model. The performance of dose accuracy by TOPAS was superior to TPS, especially for highly inhomogeneous compensator.
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Affiliation(s)
- Hongdong Liu
- Department of Physics, University of Science and Technology of China, Hefei, Anhui, China; University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
| | - Zuofeng Li
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
| | | | - Liu Hong
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
| | - Xi Pei
- Department of Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Xie George Xu
- Department of Physics, University of Science and Technology of China, Hefei, Anhui, China; Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, NY, USA.
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Schuemann J, McNamara AL, Ramos-Méndez J, Perl J, Held KD, Paganetti H, Incerti S, Faddegon B. TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology. Radiat Res 2019; 191:125-138. [PMID: 30609382 DOI: 10.1667/rr15226.1] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.
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Affiliation(s)
- J Schuemann
- a Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - A L McNamara
- a Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - J Ramos-Méndez
- b Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - J Perl
- c SLAC National Accelerator Laboratory, Menlo Park, California
| | - K D Held
- a Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - H Paganetti
- a Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - S Incerti
- d CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan, France.,e University of Bordeaux, CENBG, UMR 5797, F-33170 Gradignan, France
| | - B Faddegon
- b Department of Radiation Oncology, University of California San Francisco, San Francisco, California
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Chen Y, Grassberger C, Li J, Hong TS, Paganetti H. Impact of potentially variable RBE in liver proton therapy. Phys Med Biol 2018; 63:195001. [PMID: 30183674 PMCID: PMC6207451 DOI: 10.1088/1361-6560/aadf24] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties. This study aims to evaluate the impact of a variable proton RBE in proton therapy liver trials using different fractionation schemes. Sixteen liver cancer cases were evaluated assuming two clinical schedules of 40 Gy/5 fractions and 58.05 Gy/15 fractions. The linear energy transfer (LET) and physical dose distribution in patients were simulated using Monte Carlo. The variable RBE distribution was calculated using a phenomenological model, considering the influence of the LET, fraction size and α/β value. Further, models to predict normal tissue complication probability (NTCP) and tumor control probability (TCP) were used to investigate potential RBE effects on outcome predictions. Applying the variable RBE model to the 5 and 15 fractions schedules results in an increase in mean fraction-size equivalent dose (FED) to the normal liver of 5.0% and 9.6% respectively. For patients with a mean FED to the normal liver larger than 29.8 Gy, this results in a non-negligible increase in the predicted NTCP of the normal liver averaging 11.6%, ranging from 2.7% to 25.6%. On the other hand, decrease in TCP was less than 5% for both fractionation regimens for all patients when assuming a variable RBE instead of constant. Consequently, the difference in TCP between the two fractionation schedules did not change significantly assuming a variable RBE while the impact on the NTCP difference was highly case specific. In addition, both the NTCP and TCP decrease with increasing α/β value for both fractionation schemes, with the decreases being more pronounced when using a variable RBE compared to using RBE = 1.1. Assuming a constant RBE of 1.1 most likely overestimates the therapeutic ratio in proton therapy for liver cancer, predominantly due to underestimation of the RBE-weighted dose to the normal liver. The impact of applying a variable RBE (as compared to RBE = 1.1) on the NTCP difference of the two fractionation regimens is case dependent. A variable RBE results in a slight increase in TCP difference. Variations in patient radiosensitivity increase when using a variable RBE.
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Affiliation(s)
- Yizheng Chen
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, United States of America. Department of Engineering Physics, Tsinghua University, Beijing 100084, People's Republic of China. Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084, People's Republic of China
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Shin WG, Testa M, Kim HS, Jeong JH, Lee SB, Kim YJ, Min CH. Independent dose verification system with Monte Carlo simulations using TOPAS for passive scattering proton therapy at the National Cancer Center in Korea. Phys Med Biol 2017; 62:7598-7616. [PMID: 28809759 DOI: 10.1088/1361-6560/aa8663] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
For the independent validation of treatment plans, we developed a fully automated Monte Carlo (MC)-based patient dose calculation system with the tool for particle simulation (TOPAS) and proton therapy machine installed at the National Cancer Center in Korea to enable routine and automatic dose recalculation for each patient. The proton beam nozzle was modeled with TOPAS to simulate the therapeutic beam, and MC commissioning was performed by comparing percent depth dose with the measurement. The beam set-up based on the prescribed beam range and modulation width was automated by modifying the vendor-specific method. The CT phantom was modeled based on the DICOM CT files with TOPAS-built-in function, and an in-house-developed C++ code directly imports the CT files for positioning the CT phantom, RT-plan file for simulating the treatment plan, and RT-structure file for applying the Hounsfield unit (HU) assignment, respectively. The developed system was validated by comparing the dose distributions with those calculated by the treatment planning system (TPS) for a lung phantom and two patient cases of abdomen and internal mammary node. The results of the beam commissioning were in good agreement of up to 0.8 mm2 [Formula: see text] for B8 option in both of the beam range and the modulation width of the spread-out Bragg peaks. The beam set-up technique can predict the range and modulation width with an accuracy of 0.06% and 0.51%, respectively, with respect to the prescribed range and modulation in arbitrary points of B5 option (128.3, 132.0, and 141.2 mm2 [Formula: see text] of range). The dose distributions showed higher than 99% passing rate for the 3D gamma index (3 mm distance to agreement and 3% dose difference) between the MC simulations and the clinical TPS in the target volume. However, in the normal tissues, less favorable agreements were obtained for the radiation treatment planning with the lung phantom and internal mammary node cases. The discrepancies might come from the limitations of the clinical TPS, which is the inaccurate dose calculation algorithm for the scattering effect, in the range compensator and inhomogeneous material. Moreover, the steep slope of the compensator, conversion of the HU values to the human phantom, and the dose calculation algorithm for the HU assignment also could be reasons of the discrepancies. The current study could be used for the independent dose validation of treatment plans including high inhomogeneities, the steep compensator, and riskiness such as lung, head & neck cases. According to the treatment policy, the dose discrepancies predicted with MC could be used for the acceptance decision of the original treatment plan.
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Affiliation(s)
- Wook-Geun Shin
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Korea
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Verburg JM, Grassberger C, Dowdell S, Schuemann J, Seco J, Paganetti H. Automated Monte Carlo Simulation of Proton Therapy Treatment Plans. Technol Cancer Res Treat 2016; 15:NP35-NP46. [DOI: 10.1177/1533034615614139] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 09/11/2015] [Accepted: 10/01/2015] [Indexed: 11/15/2022] Open
Abstract
Simulations of clinical proton radiotherapy treatment plans using general purpose Monte Carlo codes have been proven to be a valuable tool for basic research and clinical studies. They have been used to benchmark dose calculation methods, to study radiobiological effects, and to develop new technologies such as in vivo range verification methods. Advancements in the availability of computational power have made it feasible to perform such simulations on large sets of patient data, resulting in a need for automated and consistent simulations. A framework called MCAUTO was developed for this purpose. Both passive scattering and pencil beam scanning delivery are supported. The code handles the data exchange between the treatment planning system and the Monte Carlo system, which requires not only transfer of plan and imaging information but also translation of institutional procedures, such as output factor definitions. Simulations are performed on a high-performance computing infrastructure. The simulation methods were designed to use the full capabilities of Monte Carlo physics models, while also ensuring consistency in the approximations that are common to both pencil beam and Monte Carlo dose calculations. Although some methods need to be tailored to institutional planning systems and procedures, the described procedures show a general road map that can be easily translated to other systems.
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Affiliation(s)
- Joost Mathijs Verburg
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephen Dowdell
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joao Seco
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Fracchiolla F, Lorentini S, Widesott L, Schwarz M. Characterization and validation of a Monte Carlo code for independent dose calculation in proton therapy treatments with pencil beam scanning. Phys Med Biol 2015; 60:8601-19. [PMID: 26501569 DOI: 10.1088/0031-9155/60/21/8601] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a method of creating and validating a Monte Carlo (MC) model of a proton Pencil Beam Scanning (PBS) machine using only commissioning measurements and avoiding the nozzle modeling. Measurements with a scintillating screen coupled with a CCD camera, ionization chamber and a Faraday Cup were used to model the beam in TOPAS without using any machine parameter information but the virtual source distance from the isocenter. Then the model was validated on simple Spread Out Bragg Peaks (SOBP) delivered in water phantom and with six realistic clinical plans (many involving 3 or more fields) on an anthropomorphic phantom. In particular the behavior of the moveable Range Shifter (RS) feature was investigated and its modeling has been proposed. The gamma analysis (3%,3 mm) was used to compare MC, TPS (XiO-ELEKTA) and measured 2D dose distributions (using radiochromic film). The MC modeling proposed here shows good results in the validation phase, both for simple irradiation geometry (SOBP in water) and for modulated treatment fields (on anthropomorphic phantoms). In particular head lesions were investigated and both MC and TPS data were compared with measurements. Treatment plans with no RS always showed a very good agreement with both of them (γ-Passing Rate (PR) > 95%). Treatment plans in which the RS was needed were also tested and validated. For these treatment plans MC results showed better agreement with measurements (γ-PR > 93%) than the one coming from TPS (γ-PR < 88%). This work shows how to simplify the MC modeling of a PBS machine for proton therapy treatments without accounting for any hardware components and proposes a more reliable RS modeling than the one implemented in our TPS. The validation process has shown how this code is a valid candidate for a completely independent treatment plan dose calculation algorithm. This makes the code an important future tool for the patient specific QA verification process.
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Affiliation(s)
- F Fracchiolla
- Azienda Provinciale per i Servizi Sanitari (APSS) Protontherapy Department, Trento, Italy. Post Graduate School of Medical Physics 'Sapienza' University of Rome, 00185 Roma, Italy
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McNamara AL, Schuemann J, Paganetti H. A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data. Phys Med Biol 2015; 60:8399-416. [PMID: 26459756 DOI: 10.1088/0031-9155/60/21/8399] [Citation(s) in RCA: 209] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies along the treatment field, i.e. with linear energy transfer (LET) and with tissue type. A recent review study collected over 70 experimental reports on proton RBE, providing a comprehensive dataset for predicting RBE for cell survival. Using this dataset we developed a model to predict proton RBE based on dose, dose average LET (LETd) and the ratio of the linear-quadratic model parameters for the reference radiation (α/β)x, as the tissue specific parameter. The proposed RBE model is based on the linear quadratic model and was derived from a nonlinear regression fit to 287 experimental data points. The proposed model predicts that the RBE increases with increasing LETd and decreases with increasing (α/β)x. This agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/β)x. The model additionally predicts a decrease in RBE with increasing dose and shows a relationship between both α and β with LETd. Our proposed phenomenological RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. Previously published phenomenological models, based on a limited data set, may have to be revised.
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Affiliation(s)
- Aimee L McNamara
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, 30 Fruit Street, Boston, MA 02114, USA
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Polster L, Schuemann J, Rinaldi I, Burigo L, McNamara AL, Stewart RD, Attili A, Carlson DJ, Sato T, Ramos Méndez J, Faddegon B, Perl J, Paganetti H. Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints. Phys Med Biol 2015; 60:5053-70. [PMID: 26061666 DOI: 10.1088/0031-9155/60/13/5053] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.
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Affiliation(s)
- Lisa Polster
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA. Experimental Radiation Oncology, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
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Ramos-Méndez J, Perl J, Schümann J, Shin J, Paganetti H, Faddegon B. A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy. Phys Med Biol 2015; 60:5037-52. [PMID: 26061583 DOI: 10.1088/0031-9155/60/13/5037] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The aim of this work was to develop a framework for modeling organ effects within TOPAS (TOol for PArticle Simulation), a wrapper of the Geant4 Monte Carlo toolkit that facilitates particle therapy simulation. The DICOM interface for TOPAS was extended to permit contour input, used to assign voxels to organs. The following dose response models were implemented: The Lyman-Kutcher-Burman model, the critical element model, the population based critical volume model, the parallel-serial model, a sigmoid-based model of Niemierko for normal tissue complication probability and tumor control probability (TCP), and a Poisson-based model for TCP. The framework allows easy manipulation of the parameters of these models and the implementation of other models. As part of the verification, results for the parallel-serial and Poisson model for x-ray irradiation of a water phantom were compared to data from the AAPM Task Group 166. When using the task group dose-volume histograms (DVHs), results were found to be sensitive to the number of points in the DVH, with differences up to 2.4%, some of which are attributable to differences between the implemented models. New results are given with the point spacing specified. When using Monte Carlo calculations with TOPAS, despite the relatively good match to the published DVH's, differences up to 9% were found for the parallel-serial model (for a maximum DVH difference of 2%) and up to 0.5% for the Poisson model (for a maximum DVH difference of 0.5%). However, differences of 74.5% (in Rectangle1), 34.8% (in PTV) and 52.1% (in Triangle) for the critical element, critical volume and the sigmoid-based models were found respectively. We propose a new benchmark for verification of organ effect models in proton therapy. The benchmark consists of customized structures in the spread out Bragg peak plateau, normal tissue, tumor, penumbra and in the distal region. The DVH's, DVH point spacing, and results of the organ effect models are provided. The models were used to calculate dose response for a Head and Neck patient to demonstrate functionality of the new framework and indicate the degree of variability between the models in proton therapy.
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Affiliation(s)
- J Ramos-Méndez
- Deparment of Radiation Oncology, University of California at San Francisco, San Francisco, CA 94143, USA
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Méndez JR, Perl J, Schümann J, Shin J, Paganetti H, Faddegon B. Improved efficiency in Monte Carlo simulation for passive-scattering proton therapy. Phys Med Biol 2015; 60:5019-35. [PMID: 26061457 DOI: 10.1088/0031-9155/60/13/5019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The aim of this work was to improve the computational efficiency of Monte Carlo simulations when tracking protons through a proton therapy treatment head. Two proton therapy facilities were considered, the Francis H Burr Proton Therapy Center (FHBPTC) at the Massachusetts General Hospital and the Crocker Lab eye treatment facility used by University of California at San Francisco (UCSFETF). The computational efficiency was evaluated for phase space files scored at the exit of the treatment head to determine optimal parameters to improve efficiency while maintaining accuracy in the dose calculation. For FHBPTC, particles were split by a factor of 8 upstream of the second scatterer and upstream of the aperture. The radius of the region for Russian roulette was set to 2.5 or 1.5 times the radius of the aperture and a secondary particle production cut (PC) of 50 mm was applied. For UCSFETF, particles were split a factor of 16 upstream of a water absorber column and upstream of the aperture. Here, the radius of the region for Russian roulette was set to 4 times the radius of the aperture and a PC of 0.05 mm was applied. In both setups, the cylindrical symmetry of the proton beam was exploited to position the split particles randomly spaced around the beam axis. When simulating a phase space for subsequent water phantom simulations, efficiency gains between a factor of 19.9 ± 0.1 and 52.21 ± 0.04 for the FHTPC setups and 57.3 ± 0.5 for the UCSFETF setups were obtained. For a phase space used as input for simulations in a patient geometry, the gain was a factor of 78.6 ± 7.5. Lateral-dose curves in water were within the accepted clinical tolerance of 2%, with statistical uncertainties of 0.5% for the two facilities. For the patient geometry and by considering the 2% and 2mm criteria, 98.4% of the voxels showed a gamma index lower than unity. An analysis of the dose distribution resulted in systematic deviations below of 0.88% for 20% of the voxels with dose of 20% of the maximum or more.
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Affiliation(s)
- J Ramos Méndez
- Department of Radiation Oncology, University of California at San Francisco, San Francisco, CA 94143, USA
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Schuemann J, Giantsoudi D, Grassberger C, Moteabbed M, Min CH, Paganetti H. Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy. Int J Radiat Oncol Biol Phys 2015; 92:1157-1164. [PMID: 26025779 DOI: 10.1016/j.ijrobp.2015.04.006] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 03/13/2015] [Accepted: 04/02/2015] [Indexed: 11/29/2022]
Abstract
PURPOSE To assess the impact of approximations in current analytical dose calculation methods (ADCs) on tumor control probability (TCP) in proton therapy. METHODS Dose distributions planned with ADC were compared with delivered dose distributions as determined by Monte Carlo simulations. A total of 50 patients were investigated in this analysis with 10 patients per site for 5 treatment sites (head and neck, lung, breast, prostate, liver). Differences were evaluated using dosimetric indices based on a dose-volume histogram analysis, a γ-index analysis, and estimations of TCP. RESULTS We found that ADC overestimated the target doses on average by 1% to 2% for all patients considered. The mean dose, D95, D50, and D02 (the dose value covering 95%, 50% and 2% of the target volume, respectively) were predicted within 5% of the delivered dose. The γ-index passing rate for target volumes was above 96% for a 3%/3 mm criterion. Differences in TCP were up to 2%, 2.5%, 6%, 6.5%, and 11% for liver and breast, prostate, head and neck, and lung patients, respectively. Differences in normal tissue complication probabilities for bladder and anterior rectum of prostate patients were less than 3%. CONCLUSION Our results indicate that current dose calculation algorithms lead to underdosage of the target by as much as 5%, resulting in differences in TCP of up to 11%. To ensure full target coverage, advanced dose calculation methods like Monte Carlo simulations may be necessary in proton therapy. Monte Carlo simulations may also be required to avoid biases resulting from systematic discrepancies in calculated dose distributions for clinical trials comparing proton therapy with conventional radiation therapy.
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Affiliation(s)
- Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
| | - Drosoula Giantsoudi
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Maryam Moteabbed
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Chul Hee Min
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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Giantsoudi D, Schuemann J, Jia X, Dowdell S, Jiang S, Paganetti H. Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study. Phys Med Biol 2015; 60:2257-69. [PMID: 25715661 DOI: 10.1088/0031-9155/60/6/2257] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1-2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8-2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.
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Affiliation(s)
- Drosoula Giantsoudi
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Testa M, Schümann J, Lu HM, Shin J, Faddegon B, Perl J, Paganetti H. Experimental validation of the TOPAS Monte Carlo system for passive scattering proton therapy. Med Phys 2014; 40:121719. [PMID: 24320505 DOI: 10.1118/1.4828781] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE TOPAS (TOol for PArticle Simulation) is a particle simulation code recently developed with the specific aim of making Monte Carlo simulations user-friendly for research and clinical physicists in the particle therapy community. The authors present a thorough and extensive experimental validation of Monte Carlo simulations performed with TOPAS in a variety of setups relevant for proton therapy applications. The set of validation measurements performed in this work represents an overall end-to-end testing strategy recommended for all clinical centers planning to rely on TOPAS for quality assurance or patient dose calculation and, more generally, for all the institutions using passive-scattering proton therapy systems. METHODS The authors systematically compared TOPAS simulations with measurements that are performed routinely within the quality assurance (QA) program in our institution as well as experiments specifically designed for this validation study. First, the authors compared TOPAS simulations with measurements of depth-dose curves for spread-out Bragg peak (SOBP) fields. Second, absolute dosimetry simulations were benchmarked against measured machine output factors (OFs). Third, the authors simulated and measured 2D dose profiles and analyzed the differences in terms of field flatness and symmetry and usable field size. Fourth, the authors designed a simple experiment using a half-beam shifter to assess the effects of multiple Coulomb scattering, beam divergence, and inverse square attenuation on lateral and longitudinal dose profiles measured and simulated in a water phantom. Fifth, TOPAS' capabilities to simulate time dependent beam delivery was benchmarked against dose rate functions (i.e., dose per unit time vs time) measured at different depths inside an SOBP field. Sixth, simulations of the charge deposited by protons fully stopping in two different types of multilayer Faraday cups (MLFCs) were compared with measurements to benchmark the nuclear interaction models used in the simulations. RESULTS SOBPs' range and modulation width were reproduced, on average, with an accuracy of +1, -2 and ±3 mm, respectively. OF simulations reproduced measured data within ±3%. Simulated 2D dose-profiles show field flatness and average field radius within ±3% of measured profiles. The field symmetry resulted, on average in ±3% agreement with commissioned profiles. TOPAS accuracy in reproducing measured dose profiles downstream the half beam shifter is better than 2%. Dose rate function simulation reproduced the measurements within ∼2% showing that the four-dimensional modeling of the passively modulation system was implement correctly and millimeter accuracy can be achieved in reproducing measured data. For MLFCs simulations, 2% agreement was found between TOPAS and both sets of experimental measurements. The overall results show that TOPAS simulations are within the clinical accepted tolerances for all QA measurements performed at our institution. CONCLUSIONS Our Monte Carlo simulations reproduced accurately the experimental data acquired through all the measurements performed in this study. Thus, TOPAS can reliably be applied to quality assurance for proton therapy and also as an input for commissioning of commercial treatment planning systems. This work also provides the basis for routine clinical dose calculations in patients for all passive scattering proton therapy centers using TOPAS.
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Affiliation(s)
- M Testa
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard University Medical School, Boston, Massachusetts 02114
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Schuemann J, Dowdell S, Grassberger C, Min CH, Paganetti H. Site-specific range uncertainties caused by dose calculation algorithms for proton therapy. Phys Med Biol 2014; 59:4007-31. [PMID: 24990623 DOI: 10.1088/0031-9155/59/15/4007] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The purpose of this study was to assess the possibility of introducing site-specific range margins to replace current generic margins in proton therapy. Further, the goal was to study the potential of reducing margins with current analytical dose calculations methods. For this purpose we investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict the range of proton fields. Dose distributions predicted by an analytical pencil-beam algorithm were compared with those obtained using Monte Carlo (MC) simulations (TOPAS). A total of 508 passively scattered treatment fields were analyzed for seven disease sites (liver, prostate, breast, medulloblastoma-spine, medulloblastoma-whole brain, lung and head and neck). Voxel-by-voxel comparisons were performed on two-dimensional distal dose surfaces calculated by pencil-beam and MC algorithms to obtain the average range differences and root mean square deviation for each field for the distal position of the 90% dose level (R90) and the 50% dose level (R50). The average dose degradation of the distal falloff region, defined as the distance between the distal position of the 80% and 20% dose levels (R80-R20), was also analyzed. All ranges were calculated in water-equivalent distances. Considering total range uncertainties and uncertainties from dose calculation alone, we were able to deduce site-specific estimations. For liver, prostate and whole brain fields our results demonstrate that a reduction of currently used uncertainty margins is feasible even without introducing MC dose calculations. We recommend range margins of 2.8% + 1.2 mm for liver and prostate treatments and 3.1% + 1.2 mm for whole brain treatments, respectively. On the other hand, current margins seem to be insufficient for some breast, lung and head and neck patients, at least if used generically. If no case specific adjustments are applied, a generic margin of 6.3% + 1.2 mm would be needed for breast, lung and head and neck treatments. We conclude that the currently used generic range uncertainty margins in proton therapy should be redefined site specific and that complex geometries may require a field specific adjustment. Routine verifications of treatment plans using MC simulations are recommended for patients with heterogeneous geometries.
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Affiliation(s)
- J Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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Bueno M, Paganetti H, Duch MA, Schuemann J. An algorithm to assess the need for clinical Monte Carlo dose calculation for small proton therapy fields based on quantification of tissue heterogeneity. Med Phys 2014; 40:081704. [PMID: 23927301 DOI: 10.1118/1.4812682] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE In proton therapy, complex density heterogeneities within the beam path constitute a challenge to dose calculation algorithms. This might question the reliability of dose distributions predicted by treatment planning systems based on analytical dose calculation. For cases in which substantial dose errors are expected, resorting to Monte Carlo dose calculations might be essential to ensure a successful treatment outcome and therefore the benefit is worth a presumably long computation time. The aim of this study was to define an indicator for the accuracy of dose delivery based on analytical dose calculations in treatment planning systems for small proton therapy fields to identify those patients for which Monte Carlo dose calculation is warranted. METHODS Fourteen patients treated at our facility with small passively scattered proton beams (apertures diameters below 7 cm) were selected. Plans were generated in the XiO treatment planning system in combination with a pencil beam algorithm developed at the Massachusetts General Hospital and compared to Monte Carlo dose calculations. Differences in the dose to the 50% of the gross tumor volume (D50, GTV) were assessed in a field-by-field basis. A simple and fast methodology was developed to quantify the inhomogeneity of the tissue traversed by a single small proton beam using a heterogeneity index (HI)-a concept presented by Plugfelder et al. [Med. Phys. 34, 1506-1513 (2007)] for scanned proton beams. Finally, the potential correlation between the error made by the pencil beam based treatment planning algorithm for each field and the level of tissue heterogeneity traversed by the proton beam given by the HI was evaluated. RESULTS Discrepancies up to 5.4% were found in D50 for single fields, although dose differences were within clinical tolerance levels (<3%) when combining all of the fields involved in the treatment. The discrepancies found for each field exhibited a strong correlation to their associated HI-values (Spearman's ρ=0.8, p<0.0001); the higher the level of tissue inhomogeneities for a particular field, the larger the error by the analytical algorithm. With the established correlation a threshold for HI can be set by choosing a tolerance level of 2-3%-commonly accepted in radiotherapy. CONCLUSIONS The HI is a good indicator for the accuracy of proton field delivery in terms of GTV prescription dose coverage when small fields are delivered. Each HI-value was obtained from the CT image in less than 3 min on a computer with 2 GHz CPU allowing implementation of this methodology in clinical routine. For HI-values exceeding the threshold, either a change in beam direction (if feasible) or a recalculation of the dose with Monte Carlo would be highly recommended.
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Affiliation(s)
- M Bueno
- Departament de Dosimetria i Física Mèdica, Institut de Tècniques Energètiques, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain.
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