Fast multipurpose Monte Carlo simulation for proton therapy using multi‐ and many‐core CPU architectures

Souris, Kevin; Lee, John A.; Sterpin, Edmond

doi:10.1118/1.4943377

Cited by 92 publications

(109 citation statements)

References 49 publications

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“…The routine use of TOPAS in the clinic for dose verification or treatment planning is significantly hindered by its long computation time. Therefore, a fast MC algorithm, such as MCsquare,16 has been developed for proton therapy in order to accelerate the computation speed while preserving the accuracy of a general purpose MC. MCsquare used in our study is a dedicated fast MC algorithm embedded in the open Reggui platform (https://openreggui.org/).…”

Section: Methodsmentioning

confidence: 99%

“…The aim of this work is to develop and validate an accurate dose calculation platform based on TOPAS13 that can be used to configure and validate both commercial14, 15 and in‐house16, 17 fast MC dose calculation algorithms for PBS treatment. Validation and clinical implementation of fast MC can potentially facilitate routine treatment plan quality assurance,18, 19 and bring 4D dynamic dose (4DDD) MC engines from conceptual research to clinical practices,20, 21, 22 when the dosimetric accuracy of analytical dose engines are challenged for the cases of heterogeneous tissue or with involvement of range shifter/patient bolus and large air gap.…”

Section: Introductionmentioning

confidence: 99%

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Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy

Huang

Kang

Souris

et al. 2018

J Applied Clin Med Phys

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Monte Carlo (MC)‐based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three‐dimensional dose distributions of spread‐out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of measured data required for low dose “halo” modeling, especially when a range shifter is employed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy

Huang

Kang

Souris

et al. 2018

J Applied Clin Med Phys

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“…Fippel and Soukup introduced a simplified MC algorithm for voxelized geometries which had been previously applied to photons and electrons, and Li et al developed a track‐repeating algorithm based on precomputed trajectories and interactions for various materials and energies. Some fast MC codes also increase computational efficiency by taking advantage of various modern parallel processing technologies . Recently, commercial vendors have also released fast MC codes in their TPSs (e.g., Eclipse (Varian, Palo Alto, CA, USA) and Raystation (Raysearch Lab, Stockholm, Sweden)) .…”

Section: Introductionmentioning

confidence: 99%

Validation and application of a fast Monte Carlo algorithm for assessing the clinical impact of approximations in analytical dose calculations for pencil beam scanning proton therapy

Huang

Souris

et al. 2018

Medical Physics

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Purpose Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open‐source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites. Methods First, MCsquare was validated using tissue‐mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head‐and‐neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose‐volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP). Results Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head‐and‐neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%). Conclusions Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head‐and‐neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open‐source availability.

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“…MCsquare is a fast MC code developed in‐house, designed and optimized to simulate proton PBS treatments in voxelized geometries, such as a CT image. The code is entirely written in C and it exploits multithreading and vectorization of modern processors.…”

Section: Methodsmentioning

confidence: 99%

“…The plans were created with MIROpt, our treatment planning system developed in‐house. MIROpt is coupled with the two dose engines presented in the previous section: the analytical pencil beam algorithm embedded in FoCa and MCsquare . Optimization of the spot weights is performed with the large‐scale nonlinear solver IPOPT, through its MATLAB interface.…”

Section: Methodsmentioning

confidence: 99%

Performance of a hybrid Monte Carlo‐Pencil Beam dose algorithm for proton therapy inverse planning

et al. 2017

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Fast multipurpose Monte Carlo simulation for proton therapy using multi‐ and many‐core CPU architectures

Cited by 92 publications

References 49 publications

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy

Validation and application of a fast Monte Carlo algorithm for assessing the clinical impact of approximations in analytical dose calculations for pencil beam scanning proton therapy

Performance of a hybrid Monte Carlo‐Pencil Beam dose algorithm for proton therapy inverse planning

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