Purpose: Currently no fully open source software platform exists for research in medical physics and radiation therapy. In this study, we use open source technologies to develop a cross‐platform application, “dicompyler”, for evaluating radiation therapy treatment plans and to provide a research platform extensible via third party plugins. Method and Materials: Using the high level programming language, Python and open source tools, an environment was created to import DICOM / DICOM‐RT data that can be viewed, analyzed and modified. The application can import and display full treatment plans with the corresponding dose and structures and additionally display DVH data. A plugin model was developed so that the user can extend the software as needed. The platform was tested with anonymized data from various treatment planning systems using 3DCRT, IMRT, and brachytherapy plans. Additionally, in order to test the plugin system, a DICOM tree view and anonymization features were created. Results: Isodose curves and DVH values generated in dicompyler were successfully compared against the original planning system for various treatment plans. The plugin system was used to anonymize a radiation treatment plan in preparation for teaching purposes. The application ran successfully on Windows, Mac OS X, and Linux. Conclusion: This work shows that an accessible research platform can be created using solely open source technologies and be compatible across operating systems. As DICOM is pervasive in radiation therapy, it is critical that a fully free, open source system be in place for individuals to analyze and understand the data that they work with. With an extensible system, future plugins such as treatment planning and image registration can be developed.
Purpose: The accuracy of medical physics calculations is largely limited by the availability of fast computer hardware, which is in turn strongly limited by budget considerations. This research explores the use of the Cloud Computing paradigm to replace or supplement existing computing infrastructure for medical physics calculations. Cloud computing services allow for on‐demand virtual computing clusters with a pay‐as‐you‐go pricing model. This model has the potential to make large scale Monte Carlo simulations routine in clinical and research activities. Methods and materials: To test the feasibility of medical physics calculations in a cloud computing environment, a distributed Monte Carlo dose calculation framework was implemented with Amazon's EC2 cloud computing service, the Fluka 2008.3b Monte Carlo package, a distributed processing framework written in Python, and the Hadoop MapReduce framework. A variety of relevant calculations were performed: depth dose profiles of proton, electron, and photon beams, along with a simple proton treatment plan using a voxel patient geometry (Zubal phantom). The performance of EC2 was tested by running calculations on 1 to 200 virtual nodes with the different distributed processing frameworks. EC2 prices were compared with costs of comparable local hardware. Results: Relevant medical physics calculations were successfully carried out using the EC2 service. Heavy charged particle, electron, and photon depth dose profiles and a simple treatment plan were successfully calculated remotely. Performance data demonstrated the expected 1/n speed‐up as a function of node number. Two different distributed processing schemes were implemented in the cloud. Conclusion: Our implementations demonstrate the power of the existing cloud computing model. The relatively cheap pay‐as‐you‐go pricing for infrastructure on demand offers an extremely promising new paradigm for clinical and research computing, available to anyone with a network connection.
Purpose: Antiprotons have become of interest in radiotherapy due to their higher peak to plateau dose ratio relative to protons and carbon ions, and a beneficial increase in RBE towards the Bragg peak as recently verified by experimental investigations of the AD‐4 collaboration at CERN. An obstacle limiting further research is the lack of a treatment planning system capable of concurrently optimizing the necessary modulation of intensity and energy, while accounting for the variation in biological effectiveness. Here we develop a Monte Carlo based treatment planning system for this purpose and subsequently quantify its performance. Materials and Methods: Dose kernels corresponding to different energy and source configurations were calculated using MCNPX in phantom and voxelized patient CT scans, and then converted to biological equivalent dose using depth dependent RBE weighting factors derived from theory and experiment. Linear equations were formulated for each pixel representing superposition of different kernels weighted by unknown intensities. Algorithms using constrained least square and gradient descent optimization were developed to minimize objective functions measuring the geometric correlation of the planning target volume (PTV) with the calculated distribution, yielding an optimized intensity for beams as function of energy and direction. Results: Biologically optimized treatment plans implemented on a voxelized 38 year old human were in good agreement with the input PTVs, reproducing the PTVs with a mean error of less than 2.24%. Proof of principle demonstrations were successful in producing complicated structures, such resemblance of Einstein, in water phantom with a correlation greater than 93%. Conclusions: We have developed a Monte Carlo treatment planning system for energy and intensity modulated antiproton therapy capable of incorporating depth‐dependency of the RBE, and reproducing complicated PTVs with high accuracy. The work can be readily extended to incorporate more sophisticated objective functions such as NTCP and TCP functionals.
Purpose: Recent work has shown the potential benefits of antiprotons for radiotherapy. Because of the large amount of energy released by the annihilation of antiprotons, it is of interest to understand the dosimetric properties of the peripheral field. The purpose of this research is to develop a standard method to estimate, characterize, and compare the secondary dose in the peripheral radiation field of particle beams, allowing for the assessment of relative safety and treatment suitability for a given patient. Method and Materials: The FLUKA 2008.3 Monte Carlo package was used. A simplified set of target and secondary tissue phantoms (1 cm diameter spheres) were placed in a volume of water, with secondary phantoms placed every 10 cm out to 100 cm along the beam axis and perpendicular to the beam axis at the depth of the target. The target phantom was irradiated by a pencil beam of particles (antiprotons, protons, etc.). Absorbed dose was evaluated in the target phantom and ambient dose equivalent (H*(10)) was evaluated in the secondary phantoms using factors from ICRP 74 and Pelliccioni to convert from fluence. This data was then used to determine the ratio of H*(10) to target absorbed dose. Additionally, the relative contributions by different particles to the H*(10) were measured as well as various particle fluence spectra. Results: 2–40 million primary beam particles yielded satisfactory statistics to estimate the peripheral field dose values for beams of antiprotons and protons. Conclusion: As awareness of second cancer risk grows, a standard scheme for estimating secondary dose from particle therapy will be all the more important. Our scheme allows for detailed characterization of the peripheral field and dose estimation can be made for non‐target organs by weighting dose with tissue factors.
Purpose: Experimental and theoretical studies of the potential use of antiproton beams for cancer therapy have been greatly aided by Monte Carlo based calculations. Previously published data from the AD‐4/ACE collaboration has shown excellent agreement between the Fluka Monte Carlo code and experimental absorbed dose data. In this research we investigate the suitability of the open source Geant4 Monte Carlo package for antiproton absorbed dose calculations.Methods: Ionization chamber depth‐dose data collected at the CERN Antiproton Decelerator and previously published by Bassler et al were used as the experimental benchmark. Version 9.3.p01 of Geant4 and Fluka version 2008.3b‐02 were used. The antiproton beam was incident on a 20 × 20 × 20 cm3 water phantom with parameters set to match the experimental 126 MeV beam at CERN as closely as possible. Several reference physics lists were used covering electromagnetic and nuclear physics processes.Results: Geant4 consistently placed the Bragg peak at the correct depth, but the shape of the depth‐dose curve was inaccurate overall, regardless of physics lists chosen. In some cases, the peak‐to‐plateau ratio was off by an order of magnitude with fully enabled hadronic physics. This appeared to be the result of excessive in‐flight annihilation and a corresponding overproduction of secondaries as well as a dearth of at‐rest antiproton annihilations. Conclusions: Currently Geant4 does appear to have the necessary physics implemented, however in‐flight annihilation cross sections and low energy annihilation thresholds seem to be off in its default reference or standard physics lists. We plan to help improve this by sharing data and working with Geant4 developers.
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