OBJECTIVE: To estimate the primary electron beam parameters (PEB), including energy, radial intensity distribution and average angular divergence, of the individual linear accelerator using the Monte Carlo method. METHODS: A model of the treatment head and a standard field were built by BEAMnrc, and the dose distribution was simulated in water phantoms by DOSXYZnrc to obtain the percentage depth dose curve and off-axis ratio. By debugging the parameters mentioned above until the simulation and measurement results could match. RESULTS: The simulation and measurement results could achieve the best match when the parameters mentioned above were 6.25 MeV, 0.95 mm and 0.1° respectively. CONCLUSION The PEB of a linear accelerator could have a significant impact on the output beam characteristics. Monte Carlo estimation is one of the most crucial steps in establishing an individual linear accelerator model.
Monte Carlo Method ; Particle Accelerators ; Electrons ; Radiotherapy Dosage ; Phantoms, Imaging

OBJECTIVES: To investigate the impact of incorrect specification of the working correlation structure matrix on estimated sample size in a 2×2 crossover design based on the generalized estimating equation (GEE). METHODS: Based on Monte Carlo simulation, the influence of incorrect specification of the work-related structure matrix on the sample size estimation under different conditions was evaluated after controlling the total sample size n, the proportion of subjects assigned to AB sequence (s=1) θ, the correlation coefficient ρ, and the placebo effect OR. Bias and mean square error (MSE) were used to assess the difference between the sample size estimates and the theoretical values. RESULTS: When the correctly specified working correlation structure matrix is independent, the sample size estimation effect of correctly specifying the working correlation structure matrix is better than that of incorrect specification. But when the correctly specified working correlation structure matrix is equal and the correlation coefficient is closer to 0, with other factors being smaller (n≤50, θ≤0.5, OR=2 in this article), there is a situation where the bias of the sample size estimation value for the correctly specified working correlation structure matrix is greater than the bias for the incorrectly specified working correlation structure matrix. CONCLUSIONS Under most conditions, incorrectly specifying the working correlation structure matrix can cause the estimated sample size to deviate significantly from the theoretical value, but under certain conditions, the impact of incorrectly specifying the working correlation structure matrix can be small on the estimated sample size.
Sample Size ; Monte Carlo Method ; Humans ; Cross-Over Studies ; Computer Simulation ; Research Design ; Bias

OBJECTIVE: Current mainstream PET scattering correction methods are introduced and evaluated horizontally, and finally, the existing problems and development direction of scattering correction are discussed. METHODS: Based on NeuWise Pro PET/CT products of Neusoft Medical System Co. Ltd. , the simulation experiment is carried out to evaluate the influence of radionuclide distribution out of FOV (field of view) on the scattering estimation accuracy of each method. RESULTS: The scattering events produced by radionuclide out of FOV have an obvious impact on the spatial distribution of scattering, which should be considered in the model. The scattering estimation accuracy of Monte Carlo method is higher than single scatter simulation (SSS). CONCLUSIONS Clinically, if the activity of the adjacent parts out of the FOV is high, such as brain, liver, kidney and bladder, it is likely to lead to the deviation of scattering estimation. Considering the Monte Carlo scattering estimation of the distribution of radionuclide out of FOV, it's helpful to improve the accuracy of scattering distribution estimation.
Positron Emission Tomography Computed Tomography ; Scattering, Radiation ; Computer Simulation ; Brain ; Monte Carlo Method ; Phantoms, Imaging ; Image Processing, Computer-Assisted

OBJECTIVE: To compare different methods for calculating sample size based on confidence interval estimation for a single proportion with different event incidences and precisions. METHODS: We compared 7 methods, namely Wald, AgrestiCoull add z2, Agresti-Coull add 4, Wilson Score, Clopper-Pearson, Mid-p, and Jefferys, for confidence interval estimation for a single proportion. The sample size was calculated using the search method with different parameter settings (proportion of specified events and half width of the confidence interval [ω=0.05, 0.1]). With Monte Carlo simulation, the estimated sample size was used to simulate and compare the width of the confidence interval, the coverage of the confidence interval and the ratio of the noncoverage probability. RESULTS: For a high accuracy requirement (ω =0.05), the Mid-p method and Clopper Pearson method performed better when the incidence of events was low (P < 0.15). In other settings, the performance of the 7 methods did not differ significantly except for a poor symmetry of the Wald method. In the setting of ω=0.1 with a very low p (0.01-0.05), failure of iteration occurred with nearly all the methods except for the Clopper-Pearson method. CONCLUSION Different sample size determination methods based on confidence interval estimation should be selected for single proportions with different parameter settings.
Confidence Intervals ; Sample Size ; Computer Simulation ; Monte Carlo Method ; Probability

The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.
Radiation Dosage ; Software Design ; Tomography, X-Ray Computed/methods* ; Software ; Algorithms ; Phantoms, Imaging ; Monte Carlo Method

OBJECTIVE: To explore the relationship between sample size in the groups and statistical power of ANOVA and Kruskal-Wallis test with an imbalanced design. METHODS: The sample sizes of the two tests were estimated by SAS program with given parameter settings, and Monte Carlo simulation was used to examine the changes in power when the total sample size varied or remained fixed. RESULTS: In ANOVA, when the total sample size was fixed, increasing the sample size in the group with a larger mean square error improved the statistical power, but an excessively large difference in the sample sizes between groups led to reduced power. When the total sample size was not fixed, a larger mean square error in the group with increased sample size was associated with a greater increase of the statistical power. In Kruskal-wallis test, when the total sample size was fixed, increasing the sample size in groups with large mean square errors increased the statistical power irrespective of the sample size difference between the groups; when total sample size was not fixed, a larger mean square error in the group with increased sample size resulted in an increased statistical power, and the increment was similar to that for a fixed total sample size. CONCLUSIONS The relationship between statistical power and sample size in groups is affected by the mean square error, and increasing the sample size in a group with a large mean square error increases the statistical power. In Kruskal-Wallis test, increasing the sample size in a group with a large mean square error is more cost- effective than increasing the total sample size to improve the statistical power.
Computer Simulation ; Models, Statistical ; Monte Carlo Method ; Sample Size

Fluorescence Diffuse Optical Tomography (FDOT) is significant for biomedical applications, such as medical diagnostics, drug research. The fluorescence probe distribution in biological tissues can be quantitatively and non-invasively obtained via FDOT, achieving targets positioning and detection. In order to reduce the cost of FDOT, this study designs a FDOT system based on Lattice Boltzmann forward model. The system is used to realize two functions of light propagation simulation and FDOT reconstruction, and is composed of a parameter module, an algorithm module, a result display module and a data interaction module. In order to verify the effectiveness of the platform, this study carries out the light propagation simulation experiment and the FDOT reconstruction experiment, respectively comparing the Monte Carlo (MC) light propagation simulation results and the real position of the light source to be reconstructed. Experiments show that the proposed FDOT system has good reliability and has a high promotion value.
Algorithms ; Computer Simulation ; Monte Carlo Method ; Optical Devices ; Reproducibility of Results ; Tomography, Optical

The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.
Algorithms ; Computer Simulation ; DNA ; radiation effects ; DNA Damage ; Linear Energy Transfer ; Monte Carlo Method ; Protons

OBJECTIVE: To present a theoretical analysis of how the presence of bone in interstitial brachytherapy affects dose rate distributions with MCNP4C Monte Carlo code and to prepare for the next clinical study on the dose distribution of interstitial brachytherapy in head and neck neoplasm. METHODS: Type 6711,¹²⁵I brachytherapy source was simulated with MCNP4C Monte Carlo code whose cross section library was DLC-200. The dose distribution along the transverse axis in water and dose constant were compared with the American Association of Physicists in Medicine (AAPM) TG43UI update dosimetry formalism and current literature. The validated computer code was then applied to simple homogeneous bone tissue model to determine the affected different bone tissue had on dose distribution from ¹²⁵I interstitial implant. RESULTS: ¹²⁵I brachytherapy source simulated with MCNP4C Monte Carlo code met the requirements of TG43UI report. Dose rate constant, 0.977 78 cGy/(h×U), was in agreement within 1.32% compared with the recommended value of TG43UI. There was a good agreement between TG43UI about the dosimetric parameters at distances of 1 to 10 cm along the transverse axis of the ¹²⁵I source established by MCNP4C and current published data. And the dose distribution of ¹²⁵I photon emitting source in different bone tissue was calculated. Dose-deposition capacity of photons was in decreasing order: cortical bone, spongy bone, cartilage, yellow bone marrow, red bone marrow in the same medium depth. Photons deposited significantly in traversal axis among the phantom material of cortical bone and sponge bone relevant to the dose to water. In the medium depth of 0.01 cm, 0.1 cm, and 1 cm, the dose in the cortical bone was 12.90 times, 9.72 times, and 0.30 times of water respectively. CONCLUSION This study build a ¹²⁵I source model with MCNP4C Monte Carlo code, which is validated, and could be used in subsequent study. Dose distribution of photons in different bone medium is not the same as water, and its main energy deposits in bone medium surface, so we should consider the effect of bone medium when we design the target area adjacent to the bone tissue in ¹²⁵I sources implantation plan.
Brachytherapy ; Iodine Radioisotopes ; Monte Carlo Method ; Photons ; Radiotherapy Dosage

PURPOSE: The dose of drug and the size of instrument are determined based on children's weight. We aimed to validate the finger counting method (FCM) for weight estimation in Korean children using the Monte Carlo simulation. METHODS: We estimated the weight of Korean children aged 1 to 9 years by the FCM. These measurements were compared with the weight extracted by the Monte Carlo simulation applied to the “2007 Korean Children and Adolescents Growth Standard”. Pearson correlation coefficients (r) were measured to assess the correlation between the weight extracted by the simulation and that estimated by FCM. Bland-Altman analyses were performed to assess the agreement between the weight extracted by the simulation and that estimated by FCM and 2 other well-known pediatric weight estimation formulas (the Advanced Pediatric Life Support and Luscombe formulas). RESULTS: Data regarding 9,000 children's weight selected by age and gender was randomly extracted using the simulation. We found a positive correlation between the weight estimated by the FCM and the weight extracted (in boys, r = 0.896, P < 0.001; in girls, r = 0.899, P < 0.001). The FCM tended to underestimate weight in the children aged 7 years or old. CONCLUSION: This article suggests the usefulness of FCM in weight estimation, particularly in children younger than 7 years. With appreciation of the limitation in older children, the FCM could be applied to emergency practice.
Adolescent ; Body Weight ; Child ; Emergencies ; Emergency Service, Hospital ; Female ; Fingers* ; Humans ; Methods* ; Monte Carlo Method ; Resuscitation