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Register a new userIntercomparison of Monte Carlo calculated dose enhancement ratios for gold nanoparticles irradiated by X-rays: assessing the uncertainty and correct methodology for extended beams
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Results of a Monte Carlo code intercomparison exercise for simulations of the dose enhancement from a gold nanoparticle (GNP) irradiated by X-rays have been recently reported. To highlight potential differences between codes, the dose enhancement ratios (DERs) were shown for the narrow-beam geometry used in the simulations, which leads to values significantly higher than unity over distances in the order of several tens of micrometers from the GNP surface. As it has come to our attention that the figures in our paper have given rise to misinterpretation as showing the DERs of GNPs under diagnostic X-ray irradiation, this article presents estimates of the DERs that would have been obtained with realistic radiation field extensions and presence of secondary particle equilibrium (SPE). These DER values are much smaller than those for a narrow-beam irradiation shown in our paper, and significant dose enhancement is only found within a few hundred nanometers around the GNP. The approach used to obtain these estimates required the development of a methodology to identify and, where possible, correct results from simulations whose implementation deviated from the initial exercise definition. Based on this methodology, literature on Monte Carlo simulated DERs has been critically assessed.
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Organized by the European Radiation Dosimetry Group (EURADOS), a Monte Carlo code intercomparison exercise was conducted where participants simulated the emitted electron spectra and energy deposition around a single gold nanoparticle (GNP) irradiated by X-rays. In the exercise, the participants scored energy imparted in concentric spherical shells around a spherical volume filled with gold or water as well as the spectral distribution of electrons leaving the GNP. Initially, only the ratio of energy deposition with and without GNP was to be reported. During the evaluation of the exercise, however, the data for energy deposition in the presence and absence of the GNP were also requested. A GNP size of 50 nm and 100 nm diameter was considered as well as two different X-ray spectra (50 kVp and 100kVp). This introduced a redundancy that can be used to cross-validate the internal consistency of the simulation results. In this work, evaluation of the reported results is presented in terms of integral quantities that can be benchmarked against values obtained from physical properties of the radiation spectra and materials involved. The impact of different interaction cross-section datasets and their implementation in the different Monte Carlo codes is also discussed.
The application of metal nanoparticles as sensitization materials is a common strategy that is used to study dose enhancement in radiotherapy. Recent in vitro tests have revealed that magnetic gold nanoparticles can be used in cancer therapy under a magnetic field to enhance the synergistic efficiency in radiotherapy and photothermal therapy. However, magnetic gold nanoparticles have rarely been studied as sensitization materials. In this study, we obtained further results of the sensitization properties of magnetic gold nanoparticles using the Monte Carlo method TOPAS and TOPAS-nBio. We analyzed the properties of magnetic gold nanoparticles in monoenergetic photons and brachytherapy, and we investigated whether the magnetic field contributes to the sensitization process. Our results demonstrated that the dose enhancement factor of the magnetic gold nanoparticles was 16.7% lower than that of gold nanoparticles in a single particle irradiated by monoenergetic photons. In the cell model, the difference was less than 8.1% in the cytoplasm. We revealed that the magnetic field has no detrimental effect on radiosensitization. Moreover, the sensitization properties of magnetic gold nanoparticles in a clinical brachytherapy source have been revealed for the first time.
Monte Carlo (MC) method has been recognized the most accurate dose calculation method for radiotherapy. However, its extremely long computation time impedes clinical applications. Recently, a lot of efforts have been made to realize fast MC dose calculation on GPUs. Nonetheless, most of the GPU-based MC dose engines were developed in NVidia CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a fast cross-platform MC dose engine oclMC using OpenCL environment for external beam photon and electron radiotherapy in MeV energy range. Coupled photon-electron MC simulation was implemented with analogue simulations for photon transports and a Class II condensed history scheme for electron transports. To test the accuracy and efficiency of our dose engine oclMC, we compared dose calculation results of oclMC and gDPM, our previously developed GPU-based MC code, for a 15 MeV electron beam and a 6 MV photon beam on a homogenous water phantom, one slab phantom and one half-slab phantom. Satisfactory agreement was observed in all the cases. The average dose differences within 10% isodose line of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, our dose engine oclMC was 6-17% slower than gDPM when running both codes on the same NVidia TITAN card due to both different physics particle transport models and different computational environments between CUDA and OpenCL. The cross-platform portability was also validated by successfully running our new dose engine on a set of different compute devices including an Nvidia GPU card, two AMD GPU cards and an Intel CPU card using one or four cores. Computational efficiency among these platforms was compared.
Monte Carlo (MC) simulation is considered as the most accurate method for radiation dose calculations. Accuracy of a source model for a linear accelerator is critical for the overall dose calculation accuracy. In this paper, we presented an analytical source model that we recently developed for GPU-based MC dose calculations. A key concept called phase-space-ring (PSR) was proposed. It contained a group of particles that are of the same type and close in energy and radial distance to the center of the phase-space plane. The model parameterized probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. For a primary photon PSRs, the particle direction is assumed to be from the beam spot. A finite spot size is modeled with a 2D Gaussian distribution. For a scattered photon PSR, multiple Gaussian components were used to model the particle direction. The direction distribution of an electron PSRs was also modeled as a 2D Gaussian distribution with a large standard deviation. We also developed a method to analyze a phase-space file and derive corresponding model parameters. To test the accuracy of our linac source model, dose distributions of different open fields in a water phantom were calculated using our source model and compared to those directly calculated using the reference phase-space file. The average distance-to-agreement (DTA) was within 1 mm for the depth dose in the build-up region and beam penumbra regions. The root-mean-square (RMS) dose difference was within 1.1% for dose profiles at inner and outer beam regions. The maximal relative difference of output factors was within 0.5%. Good agreements were also found in an IMRT prostate patient case and an IMRT head-and-neck case. These results demonstrated the efficacy of our source model in terms of accurately representing a reference phase-space file.
The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection. Unfortunately, by uniting these advancements, we are met with a substantial expansion in the required dose information and consequential increase to the overall computational time imposed during radiation treatment planning, which cannot be handled by existing techniques for accelerating Monte Carlo dose calculation. We propose a deep convolutional neural network approach that unlocks new levels of acceleration and accuracy with regards to post-processed Monte Carlo dose results by relying on data-driven learned representations of low-level beamlet dose distributions instead of more limited filter-based denoising techniques that only utilize the information in a single dose input. Our method uses parallel UNET branches acting on three input channels before mixing latent understanding to produce noise-free dose predictions. Our model achieves a normalized mean absolute error of only 0.106% compared with the ground truth dose contrasting the 25.7% error of the under sampled MC dose fed into the network at prediction time. Our models per-beamlet prediction time is ~220ms, including Monte Carlo simulation and network prediction, with substantial additional acceleration expected from batched processing and combination with existing Monte Carlo acceleration techniques. Our method shows promise toward enabling clinical practice of advanced treatment technologies.
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