DNA damage modeled with Geant4-DNA: effects of plasmid DNA conformation and experimental conditions

The chemical stage of the Monte Carlo track-structure (MCTS) code Geant4-DNA was extended for its use in DNA strand break (SB) simulations and compared against published experimental data. Geant4-DNA simulations were performed using pUC19 plasmids (2686 base pairs) in a buffered solution of DMSO irradiated by⁶⁰Co or¹³⁷Csγ-rays. A comprehensive evaluation of SSB yields was performed considering DMSO, DNA concentration, dose and plasmid supercoiling. The latter was measured using the super helix density value used in a Brownian dynamics plasmid generation algorithm. The Geant4-DNA implementation of the independent reaction times method (IRT), developed to simulate the reaction kinetics of radiochemical species, allowed to score the fraction of supercoiled, relaxed and linearized plasmid fractions as a function of the absorbed dose. The percentage of the number of SB after •OH + DNA and H• + DNA reactions, referred as SSB efficiency, obtained using MCTS were 13.77% and 0.74% respectively. This is in reasonable agreement with published values of 12% and 0.8%. The SSB yields as a function of DMSO concentration, DNA concentration and super helix density recreated the expected published experimental behaviors within 5%, one standard deviation. The dose response of SSB and DSB yields agreed with published measurements within 5%, one standard deviation. We demonstrated that the developed extension of IRT in Geant4-DNA, facilitated the reproduction of experimental conditions. Furthermore, its calculations were strongly in agreement with experimental data. These two facts will facilitate the use of this extension in future radiobiological applications, aiding the study of DNA damage mechanisms with a high level of detail.

TOPAS-nBio validation for simulating water radiolysis and DNA damage under low-LET irradiation

The chemical stage of the Monte Carlo track-structure simulation code Geant4-DNA has been revised and validated. The root-mean-square (RMS) empirical parameter that dictates the displacement of water molecules after an ionization and excitation event in Geant4-DNA has been shortened to better fit experimental data. The pre-defined dissociation channels and branching ratios were not modified, but the reaction rate coefficients for simulating the chemical stage of water radiolysis were updated. The evaluation of Geant4-DNA was accomplished with TOPAS-nBio. For that, we compared predicted time-dependentGvalues in pure liquid water for^·OH, e^-_aq, and H₂with published experimental data. For H₂O₂and H^·, simulation of added scavengers at different concentrations resulted in better agreement with measurements. In addition, DNA geometry information was integrated with chemistry simulation in TOPAS-nBio to realize reactions between radiolytic chemical species and DNA. This was used in the estimation of the yield of single-strand breaks (SSB) induced by¹³⁷Csγ-ray radiolysis of supercoiled pUC18 plasmids dissolved in aerated solutions containing DMSO. The efficiency of SSB induction by reaction between radiolytic species and DNA used in the simulation was chosen to provide the best agreement with published measurements. An RMS displacement of 1.24 nm provided agreement with measured data within experimental uncertainties for time-dependentGvalues and under the presence of scavengers. SSB efficiencies of 24% and 0.5% for^·OH and H^·, respectively, led to an overall agreement of TOPAS-nBio results within experimental uncertainties. The efficiencies obtained agreed with values obtained with published non-homogeneous kinetic model and step-by-step Monte Carlo simulations but disagreed by 12% with published direct measurements. Improvement of the spatial resolution of the DNA damage model might mitigate such disagreement. In conclusion, with these improvements, Geant4-DNA/TOPAS-nBio provides a fast, accurate, and user-friendly tool for simulating DNA damage under low linear energy transfer irradiation.

Report on G4-Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

BACKGROUND

Geant4 is a Monte Carlo code extensively used in medical physics for a wide range of applications, such as dosimetry, micro- and nanodosimetry, imaging, radiation protection, and nuclear medicine. Geant4 is continuously evolving, so it is crucial to have a system that benchmarks this Monte Carlo code for medical physics against reference data and to perform regression testing.

AIMS

To respond to these needs, we developed G4-Med, a benchmarking and regression testing system of Geant4 for medical physics.

MATERIALS AND METHODS

G4-Med currently includes 18 tests. They range from the benchmarking of fundamental physics quantities to the testing of Monte Carlo simulation setups typical of medical physics applications. Both electromagnetic and hadronic physics processes and models within the prebuilt Geant4 physics lists are tested. The tests included in G4-Med are executed on the CERN computing infrastructure via the use of the geant-val web application, developed at CERN for Geant4 testing. The physical observables can be compared to reference data for benchmarking and to results of previous Geant4 versions for regression testing purposes.

RESULTS

This paper describes the tests included in G4-Med and shows the results derived from the benchmarking of Geant4 10.5 against reference data.

DISCUSSION

Our results indicate that the Geant4 electromagnetic physics constructor G4EmStandardPhysics_option4 gives a good agreement with the reference data for all the tests. The QGSP_BIC_HP physics list provided an overall adequate description of the physics involved in hadron therapy, including proton and carbon ion therapy. New tests should be included in the next stage of the project to extend the benchmarking to other physical quantities and application scenarios of interest for medical physics.

CONCLUSION

The results presented and discussed in this paper will aid users in tailoring physics lists to their particular application.

Monte Carlo track-structure for the radionuclide Copper-64: characterization of S-values, nanodosimetry and quantification of direct damage to DNA

TOPAS-nBio was used to simulate, collision-to-collision, the complete trajectories of electrons in water generated during the explicit simulation of ⁶⁴Cu decay. S-values and direct damage to the DNA were calculated representing the cell (C) and the cell nucleus (N) with concentric spheres of 5 μm and 4 μm in radius, respectively. The considered 'target'←'source' configurations, including the cell surface (Cs) and cytoplasm (Cy), were: C←C, C←Cs, N←N, N←Cy and N←Cs. Ionization cluster size distributions were also calculated in a cylinder immersed in water corresponding to a DNA segment of 10 base-pairs in length (diameter 2.3 nm, length 3.4 nm), modeling a radioactive point source moving from the central axis to the edge of the cylinder. For that, the first moment (M₁) and cumulative probability of having a cluster size of 2 or more ionizations in the cylindrical volume (F₂) were obtained. Finally, the direct damage to the DNA was estimated by quantifying double-strand breaks (DSBs) using the clustering algorithm DBSCAN. The S-values obtained with TOPAS-nBio for ⁶⁴Cu were 7.879 × 10^-4 ± 5 × 10^-7, 4.351 × 10^-4 ± 6 × 10^-7, 1.442 × 10^-3 ± 1 × 10^-6, 2.596 × 10^-4 ± 8 × 10^-7, 1.127 × 10^-4 ± 4 × 10^-7 Gy Bq-s^-1 for the configurations C←C, C←Cs, N←N, N←Cy and N←Cs, respectively. The difference of these values, compared with previously reported S-values for ⁶⁴Cu with the code MNCP and software MIRDCell, ranged from -4% to -25% for the configurations N←N and N←Cs, respectively. On the other hand, F₂ was maximum with the source at the center of the cylinder 0.373 ± 0.001, and monotonically decreased until reaching a value of 0.058 ± 0.001 at 2.3 nm. The same behavior was observed for M₁ with values ranging from 2.188 ± 0.004 to 0.242 ± 0.002. Finally, the DBSCAN algorithm showed that the mean number of DNA DSBs per decay were 0.187 ± 0.001, 0.0317 ± 0.0005, and 0.0125 ± 0.0002 DSB-(Bq-s)^-1 for the configurations N←N, N←Cs, and N←Cy, respectively. In conclusion, the results of the S-values show that the absorbed dose strongly depends on the distribution of the radionuclide in the cell, the dose being higher when ⁶⁴Cu is internalized in the cell nucleus, which is reinforced by the nanodosimetric study by the presence of DNA DSBs attributable to the Auger electrons emitted during the decay of ⁶⁴Cu.

TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology

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.

A New Standard DNA Damage (SDD) Data Format

Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

Monte Carlo simulation of chemistry following radiolysis with TOPAS-nBio

Simulation of water radiolysis and the subsequent chemistry provides important information on the effect of ionizing radiation on biological material. The Geant4 Monte Carlo toolkit has added chemical processes via the Geant4-DNA project. The TOPAS tool simplifies the modeling of complex radiotherapy applications with Geant4 without requiring advanced computational skills, extending the pool of users. Thus, a new extension to TOPAS, TOPAS-nBio, is under development to facilitate the configuration of track-structure simulations as well as water radiolysis simulations with Geant4-DNA for radiobiological studies. In this work, radiolysis simulations were implemented in TOPAS-nBio. Users may now easily add chemical species and their reactions, and set parameters including branching ratios, dissociation schemes, diffusion coefficients, and reaction rates. In addition, parameters for the chemical stage were re-evaluated and updated from those used by default in Geant4-DNA to improve the accuracy of chemical yields. Simulation results of time-dependent and LET-dependent primary yields Gx (chemical species per 100 eV deposited) produced at neutral pH and 25 °C by short track-segments of charged particles were compared to published measurements. The LET range was 0.05-230 keV µm-1. The calculated Gx values for electrons satisfied the material balance equation within 0.3%, similar for protons albeit with long calculation time. A smaller geometry was used to speed up proton and alpha simulations, with an acceptable difference in the balance equation of 1.3%. Available experimental data of time-dependent G-values for [Formula: see text] agreed with simulated results within 7%  ±  8% over the entire time range; for [Formula: see text] over the full time range within 3%  ±  4%; for H2O2 from 49%  ±  7% at earliest stages and 3%  ±  12% at saturation. For the LET-dependent Gx, the mean ratios to the experimental data were 1.11  ±  0.98, 1.21  ±  1.11, 1.05  ±  0.52, 1.23  ±  0.59 and 1.49  ±  0.63 (1 standard deviation) for [Formula: see text], [Formula: see text], H2, H2O2 and [Formula: see text], respectively. In conclusion, radiolysis and subsequent chemistry with Geant4-DNA has been successfully incorporated in TOPAS-nBio. Results are in reasonable agreement with published measured and simulated data.

Technical Note: Preferred dosimeter size and associated correction factors in commissioning high dose per pulse, flattening filter free x-ray beams

PURPOSE

High dose rate flattening filter free (FFF) beams pose new challenges and considerations for accurate reference and relative dosimetry. The authors report errors associated with commonly used ion chambers and introduce simple methods to mitigate them.

METHODS

Dosimetric errors due to (1) ion recombination effects of high dose per pulse (DPP) FFF beams and (2) volume-averaging effects of the radial profile were examined on a TrueBeam STx. Four commonly used cylindrical ion chambers spanning a range of lengths (0.29-2.3 cm) and volumes (0.016-0.6 cm(3)) were used to determine the magnitude of these effects for 6 and 10 MV unflattened x-ray beams (6XFFF and 10XFFF, respectively). Two methods were used to determine the magnitude of ion collection efficiency: (1) direct measurement of the percent depth dose (PDD) for the clinical, high DPP beam in comparison to that obtained after reducing the DPP and (2) measurement of Pion as a function of depth. Two methods were used to quantify the magnitude of volume-averaging: (1) direct measurement of volume-averaging via cross-calibration and (2) calculation of volume-averaging from radial profiles of the beam. Finally, a simple analytical expression for the radial profile volume-averaging correction factor, Prp = [OAR(0.29L)](-1), or the inverse of the off-axis ratio of dose at 0.29L, where L is the length of the chamber's sensitive volume, is introduced to mitigate the volume-averaging effect in Farmer-type chambers.

RESULTS

Errors in measured PDD for the clinical beams were 1.3% ± 0.07% and 1.6% ± 0.07% at 35 cm depth for the 6XFFF and 10XFFF beam, respectively, using an IBA CC13 ion chamber, due to charge recombination with a high DPP. Volume-averaging effects were 0.4% and 0.7% for the 6XFFF and 10XFFF beam, respectively, when measured with a Farmer-type chamber. For the application of TG-51, these errors combine when using a CC13 to measure the PDD and a Farmer for absolute output dosimetry for a total error of up to 2% at dmax for the 10XFFF beam.

CONCLUSIONS

Relative and absolute dosimetry in high DPP, unflattened x-ray beams of 10 MV or higher requires corrections for charge recombination and/or volume-averaging when dosimeters with certain geometries are used. Chambers used for PDD measurement are available that do not require a correction for charge recombination. A simple analytical expression of the correction factor Prp was introduced in this work to account for volume-averaging effects in Farmer chambers. Choice of an appropriate dosimeter coupled with application of the established correction factors Pion and Prp reduces the uncertainty in the PDD measurement and the reference dose measurement.

A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy

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.

Improved efficiency in Monte Carlo simulation for passive-scattering proton therapy

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.

Experimental validation of the TOPAS Monte Carlo system for passive scattering proton therapy

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.

TOPAS: an innovative proton Monte Carlo platform for research and clinical applications

PURPOSE

While Monte Carlo particle transport has proven useful in many areas (treatment head design, dose calculation, shielding design, and imaging studies) and has been particularly important for proton therapy (due to the conformal dose distributions and a finite beam range in the patient), the available general purpose Monte Carlo codes in proton therapy have been overly complex for most clinical medical physicists. The learning process has large costs not only in time but also in reliability. To address this issue, we developed an innovative proton Monte Carlo platform and tested the tool in a variety of proton therapy applications.

METHODS

Our approach was to take one of the already-established general purpose Monte Carlo codes and wrap and extend it to create a specialized user-friendly tool for proton therapy. The resulting tool, TOol for PArticle Simulation (TOPAS), should make Monte Carlo simulation more readily available for research and clinical physicists. TOPAS can model a passive scattering or scanning beam treatment head, model a patient geometry based on computed tomography (CT) images, score dose, fluence, etc., save and restart a phase space, provides advanced graphics, and is fully four-dimensional (4D) to handle variations in beam delivery and patient geometry during treatment. A custom-designed TOPAS parameter control system was placed at the heart of the code to meet requirements for ease of use, reliability, and repeatability without sacrificing flexibility.

RESULTS

We built and tested the TOPAS code. We have shown that the TOPAS parameter system provides easy yet flexible control over all key simulation areas such as geometry setup, particle source setup, scoring setup, etc. Through design consistency, we have insured that user experience gained in configuring one component, scorer or filter applies equally well to configuring any other component, scorer or filter. We have incorporated key lessons from safety management, proactively removing possible sources of user error such as line-ordering mistakes. We have modeled proton therapy treatment examples including the UCSF eye treatment head, the MGH stereotactic alignment in radiosurgery treatment head and the MGH gantry treatment heads in passive scattering and scanning modes, and we have demonstrated dose calculation based on patient-specific CT data. Initial validation results show agreement with measured data and demonstrate the capabilities of TOPAS in simulating beam delivery in 3D and 4D.

CONCLUSIONS

We have demonstrated TOPAS accuracy and usability in a variety of proton therapy setups. As we are preparing to make this tool freely available for researchers in medical physics, we anticipate widespread use of this tool in the growing proton therapy community.

SU-E-T-500: Pencil-Beam versus Monte Carlo Based Dose Calculation for Proton Therapy Patients with Complex Geometries. Clinical Use of the TOPAS Monte Carlo System

PURPOSE

To investigate the necessity of the verification of dose distributions using Monte Carlo (MC) simulations for proton therapy of head and neck patients and other complex patient geometries.

METHODS

TOPAS, a TOol for PArticle Simulations that makes MC simulations easy-to-use for research and clinical use and is layered on top of Geant4, has been used to simulate the treatments of head and neck patients at the Massachusetts General Hospital (MGH). The resulting dose distributions have been compared to pencil beam calculations based on the XiO treatment planning system. Dose difference distributions were used to highlight areas where the two algorithms did not agree. Dose volume histograms are utilized to investigate the overall agreement of the planned doses in target structures.

RESULTS

21 head and neck patients, both nasopharynx and spinal cord, were investigated. The field complexity ranges from a single field up to 13 fields. For all patients, the dose in the clinical target volume agrees well. Nevertheless, differences in critical structures around the targets have been observed mostly due to range differences between the two algorithms.

CONCLUSIONS

Pencil beam algorithms provide an accurate description of dose in the target volume. However, we conclude that the differences between MC simulations and pencil beam algorithms in regions outside the target for complex geometries, such as present in head and neck patients, support the necessity of routine use of MC simulations for treatment verifications before treatment. TOPAS is aiming to make such routine simulations available to all researchers and clinics. An automated interface utilizing TOPAS to enable such simulations has been developed at MGH and should become routinely used in the near future for patients with complex geometries. NIH/NCI R01 CA140735.

SU-E-T-478: Geometrical Splitting Technique to Improve the Computational Efficiency in Monte Carlo Calculations for Proton Therapy

PURPOSE

To implement a geometry based particle splitting technique in order to reduce the computation time when generating treatment head phase space files for proton therapy dose calculations using Monte Carlo (MC) calculations and to validate the doses generated from these phase spaces with respect to reference simulations.

METHODS

The treatment nozzles at the Francis H Burr Proton Therapy Center (FHBPTC) were modeled with a new MC tool ('TOPAS' based on Geant4). For variance reduction purposes, two particle-splitting planes were implemented, one downstream of the second ionization chamber the other upstream of the aperture of the nozzle and phase spaces in IAEA format were recovered. The symmetry of the proton beam was considered to split the particles by a factor of 4 per plane. Split particles were randomly positioned at different locations rotated around the beam axis. The computational efficiency was calculated and dose profiles compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. Depth-dose curves and beam profiles were analyzed. Dose calculation in patients was simulated to compare the performance.

RESULTS

Normalized computational efficiency between 10 and 14.5 were reached. Percentage difference between dose profiles in water for simulations done with and without particle splitting is within the statistical precision of 2%, 1 standard deviation. Dose distributions for the realistic patient treatment show differences up to 4% in the regions of interest, within 2 standard deviations.

CONCLUSIONS

By considering the cylindrically symmetric region of the nozzle and the splitting planes separated at strategic distance, considerable time reduction can be achieved without compromising the precision. This approach will reduce the time for phase space simulations for clinical MC dose calculation at FHBPTC by more than a factor of 10.

Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4

A key task within all Monte Carlo particle transport codes is 'navigation', the calculation to determine at each particle step what volume the particle may be leaving and what volume the particle may be entering. Navigation should be optimized to the specific geometry at hand. For patient dose calculation, this geometry generally involves voxelized computed tomography (CT) data. We investigated the efficiency of navigation algorithms on currently available voxel geometry parameterizations in the Monte Carlo simulation package Geant4: G4VPVParameterisation, G4VNestedParameterisation and G4PhantomParameterisation, the last with and without boundary skipping, a method where neighboring voxels with the same Hounsfield unit are combined into one larger voxel. A fourth parameterization approach (MGHParameterization), developed in-house before the latter two parameterizations became available in Geant4, was also included in this study. All simulations were performed using TOPAS, a tool for particle simulations layered on top of Geant4. Runtime comparisons were made on three distinct patient CT data sets: a head and neck, a liver and a prostate patient. We included an additional version of these three patients where all voxels, including the air voxels outside of the patient, were uniformly set to water in the runtime study. The G4VPVParameterisation offers two optimization options. One option has a 60-150 times slower simulation speed. The other is compatible in speed but requires 15-19 times more memory compared to the other parameterizations. We found the average CPU time used for the simulation relative to G4VNestedParameterisation to be 1.014 for G4PhantomParameterisation without boundary skipping and 1.015 for MGHParameterization. The average runtime ratio for G4PhantomParameterisation with and without boundary skipping for our heterogeneous data was equal to 0.97: 1. The calculated dose distributions agreed with the reference distribution for all but the G4PhantomParameterisation with boundary skipping for the head and neck patient. The maximum memory usage ranged from 0.8 to 1.8 GB depending on the CT volume independent of parameterizations, except for the 15-19 times greater memory usage with the G4VPVParameterisation when using the option with a higher simulation speed. The G4VNestedParameterisation was selected as the preferred choice for the patient geometries and treatment plans studied.