اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMCHIT - Monte Carlo model for proton and heavy-ion therapy
301
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We study the propagation of nucleons and nuclei in tissue-like media within a Monte Carlo Model for Heavy-ion Therapy (MCHIT) based on the GEANT4 toolkit (version 8.2). The model takes into account fragmentation of projectile nuclei and secondary interactions of produced nuclear fragments. Model predictions are validated with available experimental data obtained for water and PMMA phantoms irradiated by monoenergetic carbon-ion beams. The MCHIT model describes well (1) the depth-dose distributions in water and PMMA, (2) the doses measured for fragments of certain charge, (3) the distributions of positron emitting nuclear fragments produced by carbon-ion beams, and (4) the energy spectra of secondary neutrons measured at different angles to the beam direction. Radial dose profiles for primary nuclei and for different projectile fragments are calculated and discussed as possible input for evaluation of biological dose distributions. It is shown that at the periphery of the transverse dose profile close to the Bragg peak the dose from secondary nuclear fragments is comparable to the dose from primary nuclei.
قيم البحث
اقرأ أيضاً
Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose
of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time to achieve acceptable statistical uncertainty, impeding the MC methods from wider clinic applications. Here we introduce a convolutional neural network (CNN), termed as Monte Carlo Denoising Net (MCDNet), to achieve the acceleration of the MC dose calculations in radiotherapy, which is trained to directly predict the high-photon (noise-free) dose maps from the low-photon (noise-much) dose maps. Thirty patients with postoperative rectal cancer who accepted intensity-modulated radiation therapy (IMRT) were enrolled in this study. 3D Gamma Index Passing Rate (GIPR) is used to evaluate the performance of predicted dose maps. The experimental results demonstrate that the MCDNet can improve the GIPR of dose maps of 1x107 photons over that of 1x108 photons, yielding over 10x speed-up in terms of photon numbers used in the MC simulations of IMRT. It is of great potential to investigate the performance of this method on the other tumor sites and treatment modalities.
Depth distributions of positron-emitting nuclei in PMMA phantoms are calculated within a Monte Carlo model for Heavy-Ion Therapy (MCHIT) based on the GEANT4 toolkit (version 8.0). The calculated total production rates of $^{11}$C, $^{10}$C and $^{15}
$O nuclei are compared with experimental data and with corresponding results of the FLUKA and POSGEN codes. The distributions of e$^+$ annihilation points are obtained by simulating radioactive decay of unstable nuclei and transporting positrons in surrounding medium. A finite spatial resolution of the Positron Emission Tomography (PET) is taken into account in a simplified way. Depth distributions of $beta^+$-activity as seen by a PET scanner are calculated and compared to available data for PMMA phantoms. The calculated $beta^+$-activity profiles are in good agreement with PET data for proton and $^{12}$C beams at energies suitable for particle therapy. The MCHIT capability to predict the $beta^+$-activity and dose distributions in tissue-like materials of different chemical composition is demonstrated.
Purpose: To assess the effects of brain movements induced by heartbeat on dose distributions in synchrotron micro- and mini-beam radiaton therapy and to develop a model to help guide decisions and planning for future clinical trials. Methods: The Mon
te Carlo code PENELOPE was used to simulate the irradiation of a human head phantom with a variety of micro- and mini-beam arrays, with beams narrower than 100mum and above 500mum, respectively, and with radiation fields of 1cmx2cm and 2cmx2cm. The dose in the phantom due to these beams was calculated by superposing the dose profiles obtained for a single beam of 1mumx2cm. A parameter delta, accounting for the total displacement of the brain during the irradiation and due to the cardio-synchronous pulsation, was used to quantify the impact on peak-to-valley dose ratios and the full-width at half-maximum. Results: The difference between the maximum (at the phantom entrance) and the minimum (at the phantom exit) values of the peak-to-valley dose ratio reduces when the parameter $delta$ increases. The full-width at half-maximum remains almost constant with depth for any $delta$ value. Sudden changes in the two quantities are observed at the interfaces between the various tissues (brain, skull and skin) present in the head phantom. The peak-to-valley dose ratio at the center of the head phantom reduces when delta increases, remaining above 70% of the static value only for mini-beams and delta smaller than ~200mum. Conclusions: Optimal setups for brain treatments with synchrotron radiation micro- and mini-beam combs depend on the brain displacement due to cardio-synchronous pulsation. Peak-to-valley dose ratios larger than 90% of the maximum values obtained in the static case occur only for mini-beams and relatively large dose rates.
We recently built an analytical source model for GPU-based MC dose engine. In this paper, we present a sampling strategy to efficiently utilize this source model in GPU-based dose calculation. Our source model was based on a concept of phase-space-ri
ng (PSR). This ring structure makes it effective to account for beam rotational symmetry, but not suitable for dose calculations due to rectangular jaw settings. Hence, we first convert PSR source model to its phase-space let (PSL) representation. Then in dose calculation, different types of sub-sources were separately sampled. Source sampling and particle transport were iterated. So that the particles being sampled and transported simultaneously are of same type and close in energy to alleviate GPU thread divergence. We also present an automatic commissioning approach to adjust the model for a good representation of a clinical linear accelerator . Weighting factors were introduced to adjust relative weights of PSRs, determined by solving a quadratic minimization problem with a non-negativity constraint. We tested the efficiency gain of our model over a previous source model using PSL files. The efficiency was improved by 1.70 ~ 4.41, due to the avoidance of long data reading and transferring. The commissioning problem can be solved in ~20 sec. Its efficacy was tested by comparing the doses computed using the commissioned model and the uncommissioned one, with measurements in different open fields in a water phantom under a clinical Varian Truebeam 6MV beam. For the depth dose curves, the average distance-to-agreement was improved from 0.04~0.28 cm to 0.04~0.12 cm for build-up region and the root-mean-square (RMS) dose difference after build-up region was reduced from 0.32%~0.67% to 0.21%~0.48%. For lateral dose profiles, RMS difference was reduced from 0.31%~2.0% to 0.06%~0.78% at inner beam and from 0.20%~1.25% to 0.10%~0.51% at outer beam.
Heavy-ion therapy, particularly using scanned (active) beam delivery, provides a precise and highly conformal dose distribution, with maximum dose deposition for each pencil beam at its endpoint (Bragg peak), and low entrance and exit dose. To take f
ull advantage of this precision, robust range verification methods are required; these methods ensure that the Bragg peak is positioned correctly in the patient and the dose is delivered as prescribed. Relative range verification allows intra-fraction monitoring of Bragg peak spacing to ensure full coverage with each fraction, as well as inter-fraction monitoring to ensure all fractions are delivered consistently. To validate the proposed filtered Interaction Vertex Imaging method for relative range verification, a ${}^{16}$O beam was used to deliver 12 Bragg peak positions in a 40 mm poly-(methyl methacrylate) phantom. Secondary particles produced in the phantom were monitored using position-sensitive silicon detectors. Events recorded on these detectors, along with a measurement of the treatment beam axis, were used to reconstruct the sites of origin of these secondary particles in the phantom. The distal edge of the depth distribution of these reconstructed points was determined with logistic fits, and the translation in depth required to minimize the $chi^2$ statistic between these fits was used to compute the range shift between any two Bragg peak positions. In all cases, the range shift was determined with sub-millimeter precision, to a standard deviation of 200 $mu$m. This result validates filtered Interaction Vertex Imaging as a reliable relative range verification method, which should be capable of monitoring each energy step in each fraction of a scanned heavy-ion treatment plan.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
