J Schümann

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.

Range verification of passively scattered proton beams based on prompt gamma time patterns

We propose a proton range verification technique for passive scattering proton therapy systems where spread out Bragg peak (SOBP) fields are produced with rotating range modulator wheels. The technique is based on the correlation of time patterns of the prompt gamma ray emission with the range of protons delivering the SOBP. The main feature of the technique is the ability to verify the proton range with a single point of measurement and a simple detector configuration. We performed four-dimensional (time-dependent) Monte Carlo simulations using TOPAS to show the validity and accuracy of the technique. First, we validated the hadronic models used in TOPAS by comparing simulations and prompt gamma spectrometry measurements published in the literature. Second, prompt gamma simulations for proton range verification were performed for the case of a water phantom and a prostate cancer patient. In the water phantom, the proton range was determined with 2 mm accuracy with a full ring detector configuration for a dose of ~2.5 cGy. For the prostate cancer patient, 4 mm accuracy on range determination was achieved for a dose of ~15 cGy. The results presented in this paper are encouraging in view of a potential clinical application of the technique.

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy

PURPOSE To present the implementation and validation of a geometrical based variance reduction technique for the calculation of phase space data for proton therapy dose calculation. METHODS The treatment heads at the Francis H Burr Proton Therapy Center were modeled with a new Monte Carlo tool (TOPAS based on Geant4). For variance reduction purposes, two particle-splitting planes were implemented. First, the particles were split upstream of the second scatterer or at the second ionization chamber. Then, particles reaching another plane immediately upstream of the field specific aperture were split again. In each case, particles were split by a factor of 8. At the second ionization chamber and at the latter plane, the cylindrical symmetry of the proton beam was exploited to position the split particles at randomly spaced locations rotated around the beam axis. Phase space data in IAEA format were recorded at the treatment head exit and the computational efficiency was calculated. Depth-dose curves and beam profiles were analyzed. Dose distributions were compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. In addition, dose in two patients was simulated with and without particle splitting to compare the efficiency and accuracy of the technique. RESULTS A normalized computational efficiency gain of a factor of 10-20.3 was reached for phase space calculations for the different treatment head options simulated. Depth-dose curves and beam profiles were in reasonable agreement with the simulation done without splitting: within 1% for depth-dose with an average difference of (0.2 ± 0.4)%, 1 standard deviation, and a 0.3% statistical uncertainty of the simulations in the high dose region; 1.6% for planar fluence with an average difference of (0.4 ± 0.5)% and a statistical uncertainty of 0.3% in the high fluence region. The percentage differences between dose distributions in water for simulations done with and without particle splitting were within the accepted clinical tolerance of 2%, with a 0.4% statistical uncertainty. For the two patient geometries considered, head and prostate, the efficiency gain was 20.9 and 14.7, respectively, with the percentages of voxels with gamma indices lower than unity 98.9% and 99.7%, respectively, using 2% and 2 mm criteria. CONCLUSIONS The authors have implemented an efficient variance reduction technique with significant speed improvements for proton Monte Carlo simulations. The method can be transferred to other codes and other treatment heads.

TU‐C‐BRB‐08: TOPAS: A Fast and Easy to Use Tool for Particle Simulation

Purpose: Provide the proton therapy community a comprehensive free software tool to improve usability of Monte Carlo simulation for patient safety, research, QA and clinical applications. Methods: TOPAS incorporates the already‐proven Geant4 simulation toolkit into a comprehensive architecture for treatment delivery system simulations and patient calculations. Treatment Head Geometry, Patient Handling, Imaging and Scoring become both flexible and easy to use. Users import DICOM, perform automatic HU conversion, use pre‐defined components (range modifier wheels, propellers, steering magnets, jaws, etc.), adjust components or add new components. TOPAS handles time‐dependence such as component motion and beam current modulation. Beam input can come from parameterized sources or IAEA‐compliant phase space. Output includes 3D dose, phase space, high quality graphics and more. TOPAS puts all this functionality under a comprehensive Parameters Control System that simplifies research and clinical workflow. Settings validated during research can be reliably locked in to translate the setup to repeatable QA or clinical applications. Results: Using the examples of the MGH gantry treatment delivery system, the MGH radiosurgery delivery system and the UC Davis eye treatment delivery system, we demonstrate the versatility of TOPAS. The Parameters System contains all setup information in a well‐defined way to build these beamlines or others. Ability of the user to make common simulation mistakes is minimized through comprehensive and sophisticated architecture. Many checks are performed automatically, such as insisting all numbers have appropriate units. Patient calculations match those previously obtained with conventionally‐built Geant4 simulations. Conclusions: TOPAS has enhanced usability of Monte Carlo simulation while reducing possibilities for user error. TOPAS has begun to replace in‐house Monte Carlo research codes at MGH and UCSF. A second phase of TOPAS has recently begun to speed up simulation, through code profiling and variance reduction techniques. TOPAS will be free to all interested Beta testers by Fall 2012. TOPAS is supported by the US National Institutes of Health under contract number 1R01CA140735‐01

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.

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 DVHs, 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 DVHs, 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.

TU-E-BRB-05: Streamlining Monte Carlo Dose Calculations for Routing Clinical Use in Proton Therapy

Purpose: To design and validate a tool for routine verifications of clinical treatment plans using Monte Carlo(MC) simulations.Methods: We focus on the clinical implementation for patient dose calculations using TOPAS (TOol for PArticle Simulation), a user‐friendly interface to the Geant4 MC toolkit. Gantry and patient positioning follow guidelines of the IEC and phase‐space output is compliant with the IAEA format. Different parameterization schemes for voxel navigation have been tested to improve computing speed. Patient geometry and alignment are controlled by text files. CT slice input is currently supported for DICOM and XiO data files. Delivered doses, particle fluxes, energy deposits and many other properties can be scored. This new tool is now available for routine protontreatment planning at MGH. In many instances MC studies are preferred over pencil‐beam algorithms. Results: We present results from a cohort of MGH patients. A reduction in CPU time of up to 30% in patient dose calculations has been achieved with a specialized voxel navigation for CT volumes with 30 million voxels. Clinically required accuracy has been reached and verified in a water phantom with uncertainties of +1/−1.5mm in range, 3mm in modulation width and 2% for the flatness of spread‐out Bragg‐peaks with a variance comparable to measurements. For patient dose calculations, up to 8% of the voxels in patients were found to have larger than 2% and 2mm difference when comparing MC and a pencil‐beam algorithm. Conclusions: Monte Carlo simulations are still an exception in treatment planning, requiring detailed work by medical physicists who are also MCsoftware experts. TOPAS supports an easy‐to‐use interface to set up nozzle and patient simulations without programming knowledge. This study demonstrates routine patient simulations with the MC method and supports the value and feasibility of using MC simulation as a standard in protontreatment planning. The project described was supported by Award Number R01CA140735 (“PBeam: A Fast and Easy to Use Monte Carlo System for Proton Therapy”) from the National Cancer Institute.

TU-G-BRB-02: Comprehensive Handling of Time-Dependent Quantities in Scanning Beam Simulation

Purpose: To provide a comprehensive and flexible method for handling time dependent quantities including interplay effects in proton beam scanning. Methods: The time feature was devised to handle time‐dependent quantities in the Monte Carlo simulation. Each time‐dependent quantity corresponds to one time feature. A time feature can be expressed as a simple mathematical function or composed of more complex, pre‐defined functions. The functions may be combined concurrently and/or consecutively so that users can customize complex time features. During simulation, time is set in one of two manners: 1. Randomly chosen time, suitable for handling continuous behaviors, or 2. sequentially‐generated time, where time interval is equally divided. This time feature has been incorporated in TOPAS, a tool for particle simulation with Geant4, under development to make Monte Carlo simulation more accessible to both clinical and research physicists. Results: The time feature has been deployed to simulate various proton beam scanning patterns in clinical proton beam nozzle. The patterns include parallel, raster, circular, saw‐tooth, and Lissajous figure scans. Through the parameter management in TOPAS, time features for those scanning patterns were easily implemented. Dynamics of the beam including pulse shape, spot size, and movement were concurrently simulated. Randomly set time gave more accurate results when fewer histories were simulated, resulting in under‐sampling when time was set sequentially, while the simulation time with the 2 methods was similar for the same number of histories. Conclusions: Methodology has been developed to comprehensively describe time dependent behaviors in the Monte Carlo simulation and was assessed by applying to clinical proton beam scanning. A novel aspect of the method of separation of the time generators and time receivers has made it straightforward to handle multi time‐ dependent quantities and is therefore relevant to fully simulate interplay effects. The project described was supported by Award Number R01CA140735 (“PBeam: A Fast and Easy to Use Monte Carlo System for Proton Therapy”) from the National Cancer Institute.