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تسجيل مستخدم جديدFoundation of an analytical proton beamlet model for inclusion in a general proton dose calculation system
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We have developed a model for proton depth dose and lateral distributions based on Monte Carlo calculations (GEANT4) and an integration procedure of the Bethe-Bloch equation (BBE). The model accounts for the transport of primary and secondary protons, the creation of recoil protons and heavy recoil nuclei as well as lateral scattering of these contributions. The buildup, which is experimentally observed in higher energy depth dose curves, is modeled by inclusion of two different origins: 1. Secondary reaction protons with a contribution of ca. 65 % of the buildup (for monoenergetic protons). 2. Landau tails as well as Gaussian type of fluctuations for range straggling effects. All parameters of the model for initially monoenergetic proton beams have been obtained from Monte Carlo calculations or checked by them. Furthermore, there are a few parameters, which can be obtained by fitting the model to measured depth dose curves in order to describe individual characteristics of the beamline - the most important being the initial energy spread. We find that the free parameters of the depth dose model can be predicted for any intermediate energy from a couple of measured curves.
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We have developed a model for proton depth dose and lateral distributions based on Monte Carlo calculations (GEANT4) and an integration procedure of the Bethe-Bloch equation (BBE). The model accounts for the transport of primary and secondary protons
, the creation of recoil protons and heavy recoil nuclei as well as lateral scattering of these contributions. The buildup, which is experimentally observed in higher energy depth dose curves, is modeled by inclusion of two different origins: 1. Secondary reaction protons with a contribution of ca. 65 % of the buildup (for monoenergetic protons). 2. Landau tails as well as Gaussian type of fluctuations for range straggling effects. All parameters of the model for initially monoenergetic proton beams have been obtained from Monte Carlo calculations or checked by them. Furthermore, there are a few parameters, which can be obtained by fitting the model to measured depth dose curves in order to describe individual characteristics of the beamline - the most important being the initial energy spread. We find that the free parameters of the depth dose model can be predicted for any intermediate energy from a couple of measured curves.
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 uni
ting 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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