The Monte Carlo method cannot have been used for routine treatment planning because of heavy time consumption for the acceptable accuracy. Since calculation time is proportional to particle histories, we can save time by decreasing the number of histories. However, a small number of histories can cause serious uncertainties. In this study, we proposed Monte Carlo dose computation time and uncertainty reduction method using specially designed filters and adaptive denoising process. Proposed algorithm was applied to 6 MV photon and 21 MeV electron dose calculations in homogeneous and heterogeneous phantoms. Filtering time was negligible comparing to Monte Carlo simulation time. The accuracy was improved dramatically in all situations and the simulation of 1% to 10% number of histories of benchmark in photon and electron dose calculation showed the most beneficial result. The empirical reduction of necessary histories was about a factor of ten to fifty from the result.
Monte Carlo Method ; Uncertainty

The photon energy spectrum of KRISS 60Co irradiation system (AECL Eldorado 8) was calculated by means of the Monte Carlo method. The collimators were modeled realistically, and the material and dimensions of the 60Co sealed source were extracted from the source certificate given by the manufacturer. It was confirmed that the photon energy spectrum of KRISS 60Co irradiation system was in similar shape with those of NRC and BIPM.
Monte Carlo Method

Monte Carlo method was used for calculation of finite-diameter laser distribution in tissues through convolution operation. Photo-thermal ablation model was set up on the basis of Pennes bioheat equation, and tissue temperature distribution was simulated by using finite element method by ANSYS through the model. The simulation result is helpful for clinical application of laser.
Algorithms ; Finite Element Analysis ; Laser Therapy ; Monte Carlo Method ; Temperature

The dose verification methods in advanced radiotherapy are elaborated in this paper. The usage and application results for various dosimeters in dose verification are explained. As a theoretical method, Monte Carlo simulation, which has been developed greatly in recent years based on the technical progress in computer science, can be also used in dose verification with unique advantages. On the other hand, the principle of dose verification on proton and heavy-ion therapy is discussed briefly. Finally, the evaluation criteria for verification and the future development for dose verification are presented.
Humans ; Monte Carlo Method ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted

OBJECTIVETo study population pharmacokinetics of phenytoin in pediatric patients by using sparse data.

METHODSWe used routinely collected therapeutic drug monitoring data, derived from the steady state serum concentrations of phenytoin in 42 pediatric outpatients with epilepsy. Depending on whether the patients were administered with phenytoin alone or coadministered with phenobabital or clonazepam, the subjects were divided into two groups: phenytoin group and coadministration group. The population parameter and individual parameter of phenytoin in children were estimated using Monte Carlo method.

RESULTSThe children's phenytoin population pharmacokinetic parameters Vm and Km were 9.8 mg.kg(-1).d(-1) and 2.73 mg/L in phenytoin group; and 9.2 mg.kg(-1).d(-1) and 3.24 mg/L in coadministration group. There were good relationship between predicted and determined concentrations with correlation coefficient of 0.999 and 0.984, respectively.

CONCLUSIONThe coadministration of phenobarbital or clonazepam obviously affected the pharmacokinetics of phenytoin. The population pharmacokinetics of phenytoin in children may provide a usefull index for individualization of dosage regimen.

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

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

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

OBJECTIVETo discuss the method for multiple comparisons of categorical data and propose an approach to deal with the percentage data.

METHODSThe method of multiple comparisons for percentages was verified based on Bonferroni methodology and Monte Carlo method using SAS 9.13 software.

RESULTSThe type I error could be enlarged if the statistical tests were conducted without adjustment of the significant level after dividing the data of several categories or percentages into several four-fold tables. For the percentage data, the correction of adjustment of the significant level was the number of pairwise comparison minus one, as supported by the results of Monte Carlo simulation.

CONCLUSIONMultiple comparisons of categorical data should be applied appropriately. Multiple comparisons of percentages data need to be conducted with the number of pairwise comparison minus one.

Micro- and integrated biosensor provides a powerful means for cell electrophysiology research. The technology of electroplating platinum black on the electrode can uprate signal-to-noise ratio and sensitivity of the sensor. For quantifying analysis of the processing method of electroplating process, this paper proposes a grid search algorithm based on the Monte-Carlo model. The paper also puts forward the operational optimization strategy, which can rapidly implement the process of large-scale nanoparticles with different particle size of dispersion (20-200 nm) attac- hing to the electrode and shortening a simulation time from average 20 hours to 0.5 hour when the test number is 10 and electrode radius is 100 microm. When the nanoparticle was in a single layer or multiple layers, the treatment uniformity and attachment rate was analyzed by using the grid search algorithm with different sizes and shapes of electrode. Simulation results showed that under ideal conditions, when the electrode radius is less than 100 /m, with the electrode size increasing, it has an obvious effect for the effective attachment and the homogeneity of nanoparticle, which is advantageous to the quantitative evaluation of electrode array's repeatability. Under the condition of the same electrode area, the best attachment is on the circular electrode compared to the attachments on the square and rectangular ones.
Algorithms ; Biosensing Techniques ; Electrodes ; Models, Theoretical ; Monte Carlo Method ; Nanoparticles ; Particle Size ; Platinum