用于Monte Carlo模拟的复杂生物组织模型

Monte Carlo（MC）模拟被广泛应用于光子在生物组织中的传输研究。通常模拟时将生物组织近似为均匀的平板分层介质,当层状生物组织中含有异常物质（如肿瘤细胞等）或正常生物组织为非平板的复杂结构时,其模拟中的组织模型将会有相应的改变。通过探讨这几类生物组织的MC模拟模型,总结并分析模型建立的关键问题,对基于MC模拟的各种生物组织光学检测研究提供了指导。     

Dosimetric and Monte Carlo verification of jaws-only IMRT plans calculated by the Collapsed Cone Convolution algorithm for head and neck cancers

AimThe aim of this study is to verify the Prowess Panther jaws-only intensity modulated radiation therapy (JO-IMRT) treatment planning (TP) by comparing the TP dose distributions for head-and-neck (H&N) cancer with the ones simulated by Monte Carlo (MC).BackgroundTo date, dose distributions planned using JO-IMRT for H&N patients were found superior to the corresponding three-dimensional conformal radiotherapy (3D-CRT) plans. Dosimetry of the JO-IMRT plans were also experimentally verified using an ionization chamber, MapCHECK 2, and Octavius 4D and good agreements were shown.Materials and methodsDose distributions of 15 JO-IMRT plans of nasopharyngeal patients were recalculated using the EGSnrc Monte Carlo code. The clinical photon beams were simulated using the BEAMnrc. The absorbed dose to patients treated by fixed-field IMRT was computed using the DOSXYZnrc. The simulated dose distributions were then compared with the ones calculated by the Collapsed Cone Convolution (CCC) algorithm on the TPS, using the relative dose error comparison and the gamma index using global methods implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm, 1%/1 mm criteria.ResultsThere is a good agreement between the MC and TPS dose. The average gamma passing rates were 93.3 ± 3.1%, 92.8 ± 3.2%, 92.4 ± 3.4% based on the 3%/3 mm, 2%/2 mm, 1%/1 mm criteria, respectively.ConclusionsAccording to the results, it is concluded that the CCC algorithm was adequate for most of the IMRT H&N cases where the target was not immediately adjacent to the critical structures.     

Shielding disk position in intra-operative electron radiotherapy (IOERT): A Monte Carlo study

PurposeIn IOERT breast treatments, a shielding disk is frequently used to protect the underlying healthy structures. The disk is usually composed of two materials, a low-Z material intended to be oriented towards the beam and a high-Z material. As tissues are repositioned around the shield before treatment, the disk is no longer visible and its correct alignment with respect to the beam is guaranteed. This paper studies the dosimetric characteristics of four possible clinical positioning scenarios of the shielding disk. A new alignment method for the shielding disk in the beam is introduced. Finally, it suggests a new design for the shielding disk.MethodsAs the first step, the IOERT machine “Mobetron 1000” was modeled by using Monte Carlo simulation, tuning the MC model until an excellent match with the measured PDDs and profiles was achieved. Four possible shielding disk positioning scenarios were considered, determining the dosimetric impact. Furthermore, in our center, to prevent beam misalignment, we have developed a shielding disk equipped with guiding rods. Having ascertained a correct alignment between the disk and the beam, we can propose a new internal design of the shielding disk that can improve the dose distribution with a better coverage of the treated area.ResultsAll MC simulations were performed with a 12 MeV beam, the maximum energy of Mobetron 1000 and a 5.5 cm diameter flat tip applicator, this applicator being the most clinically used. The simulations were compared with measurements performed in a water phantom and showed good results within 2.2% of root mean square difference (RMSD). The misplacement positions of the shielding disk have dosimetric impacts in the treatment volume and a small translation could have a significant influence on healthy tissues. The D-scenario is the worst which could happens when the shielding disk is flipped upside down, giving up to 144% dose instead of 90% at the surface of the Pb/Al shielding disk. A new shielding design used, together with our alignment tool, is able to give a more homogeneous dose in the target area.ConclusionsThe accuracy of shielding disk position can still be problematic in IOERT dosimetry. Any method that can ascertain the good alignment between the shielding disk and the beam is beneficial for the dose distribution and is a prerequisite for an optimized shield internal design that could improve the coverage of the treated area and the protection of healthy tissues.     

Monte Carlo dose verification of VMAT treatment plans using Elekta Agility 160-leaf MLC

In this study, we verified volumetric modulated arc therapy (VMAT) plans in an Elekta Synergy system with an integrated Agility 160-leaf multileaf collimator (MLC) by comparing them with Monte Carlo (MC)-calculated dose distributions using the AAPM TG-119 structure sets. The head configuration of the linear accelerator with the integrated MLC was simulated with the EGSnrc/BEAMnrc code. Firstly, the dosimetric properties of the MLC were evaluated with the MC technique and film measurements. Next, VMAT plans were created with the Pinnacle³ treatment planning system (TPS) for four regions in the AAPM TG-119 structures. They were then verified by comparing them with MC-calculated dose distributions using dose volume histograms (DVHs) and three-dimensional (3D) gamma analysis. The MC simulations for the Agility MLC dosimetric properties were in acceptable agreement with measurements. TPS-VMAT plans using TG-119 structure sets agreed with MC dose distributions within 2% in the comparison of D₉₅ in planning target volumes (PTVs) evaluated from DVHs. In contrast, higher dose regions such as D₂₀, D₁₀, and D₅ in PTVs for TPS tended to be smaller than MC values. This tendency was particularly noticeable for mock head and neck with complicated structures. In 3D gamma analysis, the passing rates with 3%/3mm criteria in PTVs were ≥99%, except for mock head and neck (89.5%). All passing rates for organs at risk (OARs) were in acceptable agreement of >96%. It is useful to verify dose distributions of PTVs and OARs in TPS-VMAT plans by using MC dose calculations and 3D gamma analysis.     

Clinical implementation of a Monte Carlo based treatment plan QA platform for validation of Cyberknife and Tomotherapy treatments

PurposeThe main focus of the current paper is the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, without adding additional tasks to the dosimetry department.MethodsThe Monte Carlo platform consists of C++ classes for the actual functionality and a web based GUI that allows accessing the system using a web browser. Calculations are based on BEAMnrc/DOSXYZnrc and/or GATE and are performed automatically after exporting the dicom data from the treatment planning system. For Cyberknife treatments of moving targets, the log files saved during the treatment (position of robot, internal fiducials and external markers) can be used in combination with the 4D planning CT to reconstruct the actually delivered dose. The Monte Carlo platform is also used for calculation on MRI images, using pseudo-CT conversion.ResultsFor Tomotherapy treatments we obtain an excellent agreement (within 2%) for almost all cases. However, we have been able to detect a problem regarding the CT Hounsfield units definition of the Toshiba Large Bore CT when using a large reconstruction diameter. For Cyberknife treatments we obtain an excellent agreement with the Monte Carlo algorithm of the treatment planning system. For some extreme cases, when treating small lung lesions in low density lung tissue, small differences are obtained due to the different cut-off energy of the secondary electrons.ConclusionsA Monte Carlo based treatment plan validation tool has successfully been implemented in clinical routine and is used to systematically validate all Cyberknife and Tomotherapy plans.     

GATE:一种用于辐射层析成像的蒙特卡罗模拟技术

描述了GATE的主要特点以及实现这些特点的GATE所具有的层级结构。通过测试由GATE软件包自带的GATE PET Benchmark的模拟结果,验证了GATE模拟引擎的正确性以及不同初始随机数下多次模拟实验的稳定性与可靠性。     

Monte Carlo evaluation of the dose calculation algorithm of TomoTherapy for clinical cases in dynamic jaws mode

PurposeFor the TomoTherapy^® system, longitudinal conformation can be improved by selecting a smaller field width but at the expense of longer treatment time. Recently, the TomoEdge^® feature has been released with the possibility to move dynamically the jaws at the edges of the target volume, improving longitudinal penumbra and enabling faster treatments. Such delivery scheme requires additional modeling of treatment delivery. Using a previously validated Monte Carlo model (TomoPen), we evaluated the accuracy of the implementation of TomoEdge in the new dose engine of TomoTherapy for 15 clinical cases.MethodsTomoPen is based on PENELOPE. Particle tracking in the treatment head is performed almost instantaneously by 1) reading a particle from a phase-space file corresponding to the largest field and 2) correcting the weight of the particle depending on the actual jaw and MLC configurations using Monte Carlo pre-generated data. 15 clinical plans (5 head-and-neck, 5 lung and 5 prostate tumors) planned with TomoEdge and with the last release of the treatment planning system (VoLO^®) were re-computed with TomoPen. The resulting dose-volume histograms were compared.ResultsGood agreement was achieved overall, with deviations for the target volumes typically within 2% (D₉₅), excepted for small lung tumors (17 cm³) where a maximum deviation of 4.4% was observed for D₉₅. The results were consistent with previously reported values for static field widths.ConclusionsFor the clinical cases considered in the present study, the introduction of TomoEdge did not impact significantly the accuracy of the computed dose distributions.     

Configuration and validation of an analytical model predicting secondary neutron radiation in proton therapy using Monte Carlo simulations and experimental measurements

PurposeThis study focuses on the configuration and validation of an analytical model predicting leakage neutron doses in proton therapy.MethodsUsing Monte Carlo (MC) calculations, a facility-specific analytical model was built to reproduce out-of-field neutron doses while separately accounting for the contribution of intra-nuclear cascade, evaporation, epithermal and thermal neutrons. This model was first trained to reproduce in-water neutron absorbed doses and in-air neutron ambient dose equivalents, H*(10), calculated using MCNPX. Its capacity in predicting out-of-field doses at any position not involved in the training phase was also checked. The model was next expanded to enable a full 3D mapping of H*(10) inside the treatment room, tested in a clinically relevant configuration and finally consolidated with experimental measurements.ResultsFollowing the literature approach, the work first proved that it is possible to build a facility-specific analytical model that efficiently reproduces in-water neutron doses and in-air H*(10) values with a maximum difference less than 25%. In addition, the analytical model succeeded in predicting out-of-field neutron doses in the lateral and vertical direction. Testing the analytical model in clinical configurations proved the need to separate the contribution of internal and external neutrons. The impact of modulation width on stray neutrons was found to be easily adjustable while beam collimation remains a challenging issue. Finally, the model performance agreed with experimental measurements with satisfactory results considering measurement and simulation uncertainties.ConclusionAnalytical models represent a promising solution that substitutes for time-consuming MC calculations when assessing doses to healthy organs.     

Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning

PurposeThis work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.MethodsThe MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.ResultsOptimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.ConclusionsThe M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems.     

Validation of irtGPUMCD,a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy

PurposeMonte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy.MethodsThe female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on ¹⁷⁷Lu and including ^99mTc and ¹³¹I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (10⁸ photons), with gamma branching ratios of ICRP 107 publication. The ¹⁷⁷Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency.ResultsThe relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 10⁸ photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.     

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries

PurposeThis work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams.MethodsThe dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions.ResultsThe results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally <0.5 mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models.ConclusionsThe RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.     

Clinical verification of treatment planning dose calculation in lung SBRT with GATE Monte Carlo simulation code

PurposeThis study aims to use GATE/Geant4 simulation code to evaluate the performance of dose calculations with Anisotropic Analytical Algorithm (AAA) in the context of lung SBRT for complex treatments considering images of patients.MethodsFour cases of non-small cell lung cancer treated with SBRT were selected for this study. Irradiation plans were created with AAA and recalculated end to end using Monte Carlo (MC) method maintaining field configurations identical to the original plans. Each treatment plan was evaluated in terms of PTV and organs at risk (OARs) using dose-volume histograms (DVH). Dosimetric parameters obtained from DVHs were used to compare AAA and MC.ResultsThe comparison between the AAA and MC DVH using gamma analysis with the passing criteria of 3%/3% showed an average passing rate of more than 90% for the PTV structure and 97% for the OARs. Tightening the criteria to 2%/2% showed a reduction in the average passing rate of the PTV to 86%. The agreement between the AAA and MC dose calculations for PTV dosimetric parameters (V₁₀₀; V₉₀; Homogeneity index; maximum, minimum and mean dose; CI_Paddick and D_2cm) was within 18.4%. For OARs, the biggest differences were observed in the spinal cord and the great vessels.ConclusionsIn general, we did not find significant differences between AAA and MC. The results indicate that AAA could be used in complex SBRT cases that involve a larger number of small treatment fields in the presence of tissue heterogeneities.     

Monte Carlo docking simulations of cyclomaltoheptaose and dimethyl cyclomaltoheptaose with paclitaxel

The molecular basis for the remarkable enhancement of the solubility of paclitaxel by O-dimethylcyclomaltoheptaose (DM-beta-CD) over cyclomaltoheptaose (beta-cyclodextrin, beta-CD) was investigated with Monte Carlo docking-minimization simulation. As possible guests of inclusion complexation for the host cyclic oligosaccharides, two functional moieties of the suggested solution structure of paclitaxel were used where one is the C-3'N benzoyl moiety (B-ring) and the other is a hydrophobic (HP) cluster site among the C-3' phenyl (C-ring), C-2 benzoate (A-ring), and C-4 acetoxy moieties. The energetic preference of inclusion complexation of DM-beta-CD over beta-CD was analyzed on the basis of more efficient partitioning process of DM-beta-CD into the hydrophobic cluster site of the paclitaxel.     

Optical parameters of embedded abnormalities in tissues as determined by Monte Carlo simulation

Photon propagation through tissue phantoms, made of heart, adipose, and spleen tissues was simulated by Monte Carlo procedure. To detect the presence of deep-seated abnormalities, phantoms of heart with adipose and spleen tissues embedded into it were created and simulations were performed to scan the tissue surface with a source and four detector model. Profiles drawn showed variation in parameters such as backscattered intensity in regions where adipose and spleen tissues were embedded. This study shows that depending on the type of embedded tissue the backscattered fraction as measured at 2 mm from the input fiber is altered. This is enhanced for adipose and decreased for spleen tissue. This is not only shown in scanned profiles on the surface but also in constructed images.     

The effect of weekend curfews on epidemics: a Monte Carlo simulation

The ongoing COVID-19 pandemic is being responded with various methods, applying vaccines, experimental treatment options, total lockdowns or partial curfews. Weekend curfews are among the methods for reducing the number of infected persons, and this method is practically applied in some countries such as Turkey. In this study, the effect of weekend curfews on reducing the spread of a contagious disease, such as COVID-19, is modeled using a Monte Carlo algorithm with a hybrid lattice model. In the simulation setup, a fictional country with three towns and 26,610 citizens were used as a model. Results indicate that applying a weekend curfew reduces the ratio of ill cases from 0.23 to 0.15. The results also show that applying personal precautions such as social distancing is important for reducing the number of cases and deaths. If the probability of disease spread can be reduced to 0.1, in that case, the death ratio can be minimized down to 0.     

Monte Carlo Vadose Zone Model for Soil Remedial Criteria

Many vadose zone models are available for environmental remediation, but few offer the procedures for verifying model predictions with field data and for dealing with uncertainties associated with model input parameters. This article presents a modified model combining a one-dimensional vadose-zone transport model and a simple groundwater mixing model with a function of Monte Carlo simulation (MCS). The modified model is applied to determine soil remedial concentrations for methyl tertiary butyl ether (MTBE). The modified model generates a distribution of MTBE ground-water concentrations at the point of compliance. This distribution can be used to estimate the risk of exceeding groundwater quality standard given soil remedial concentrations. In a case study, soil remedial concentration for MTBE is established to be 5?µg/kg, with a 95% and 10?µg/kg with a 50% probability that groundwater concentration will not exceed the water quality objective of 13?µg/L. Furthermore, this study uses MCS to investigate uncertainties of model input parameter hydraulic conductivity (K). One set of data (K1) is based on the results of hydraulic conductivity laboratory tests, and the other (K2) is based on the results of slug tests conducted in the field. As expected, the laboratory data show smaller K values than the field data. The comparison of the MCS results obtained from the two sets of K data indicates that the MTBE groundwater concentrations calculated based on K1 are generally 160 to 625% greater than those calculated based on K2 at the same percentiles of the MCS distribution. A higher soil remedial concentration of9jig/kg is then calculated based on the MCS results from K2 at 95%ile and 19?µg/kg at 50%ile.     

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2?Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2?kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5?Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.     

Monte Carlo mixture model of lifetime cancer incidence risk from radiation exposure on shuttle and international space station

Estimating uncertainty in lifetime cancer risk for human exposure to space radiation is a unique challenge. Conventional risk assessment with low-linear-energy-transfer (LET)-based risk from Japanese atomic bomb survivor studies may be inappropriate for relativistic protons and nuclei in space due to track structure effects. This paper develops a Monte Carlo mixture model (MCMM) for transferring additive, National Institutes of Health multiplicative, and multiplicative excess cancer incidence risks based on Japanese atomic bomb survivor data to determine excess incidence risk for various US astronaut exposure profiles. The MCMM serves as an anchor point for future risk projection methods involving biophysical models of DNA damage from space radiation. Lifetime incidence risks of radiation-induced cancer for the MCMM based on low-LET Japanese data for nonleukemia (all cancers except leukemia) were 2.77 (90% confidence limit, 0.75-11.34) for males exposed to 1 Sv at age 45 and 2.20 (90% confidence limit, 0.59-10.12) for males exposed at age 55. For females, mixture model risks for nonleukemia exposed separately to 1 Sv at ages of 45 and 55 were 2.98 (90% confidence limit, 0.90-11.70) and 2.44 (90% confidence limit, 0.70-10.30), respectively. Risks for high-LET 200 MeV protons (LET=0.45 keV/micrometer), 1 MeV alpha-particles (LET=100 keV/micrometer), and 600 MeV iron particles (LET=180 keV/micrometer) were scored on a per particle basis by determining the particle fluence required for an average of one particle per cell nucleus of area 100 micrometer(2). Lifetime risk per proton was 2.68x10(-2)% (90% confidence limit, 0.79x10(-3)%-0. 514x10(-2)%). For alpha-particles, lifetime risk was 14.2% (90% confidence limit, 2.5%-31.2%). Conversely, lifetime risk per iron particle was 23.7% (90% confidence limit, 4.5%-53.0%). Uncertainty in the DDREF for high-LET particles may be less than that for low-LET radiation because typically there is very little dose-rate dependence. Probability density functions for high-LET radiation quality and dose-rate may be preferable to conventional risk assessment approaches. Nuclear reactions and track structure effects in tissue may not be properly estimated by existing data using in vitro models for estimating RBEs. The method used here is being extended to estimate uncertainty in spacecraft shielding effectiveness in various space radiation environments.