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

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.

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

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

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

Most brachytherapy treatment planning systems employ a dosimetry formalism based on the AAPM TG-43 report which does not appropriately consider tissue heterogeneity. In this study we aimed to set up a simple Monte Carlo-based intracavitary high-dose-rate brachytherapy (IC-HDRB) plan verification platform, focusing particularly on the robustness of the direct Monte Carlo dose calculation using material and density information derived from CT images. CT images of slab phantoms and a uterine cervical cancer patient were used for brachytherapy plans based on the Plato (Nucletron, Netherlands) brachytherapy planning system. Monte Carlo simulations were implemented using the parameters from the Plato system and compared with the EBT film dosimetry and conventional dose computations. EGSnrc based DOSXYZnrc code was used for Monte Carlo simulations. Each (192)Ir source of the afterloader was approximately modeled as a parallel-piped shape inside the converted CT data set whose voxel size was 2x2x2 mm3. Bracytherapy dose calculations based on the TG-43 showed good agreement with the Monte Carlo results in a homogeneous media whose density was close to water, but there were significant errors in high-density materials. For a patient case, A and B point dose differences were less than 3%, while the mean dose discrepancy was as much as 5%. Conventional dose computation methods might underdose the targets by not accounting for the effects of high-density materials. The proposed platform was shown to be feasible and to have good dose calculation accuracy. One should be careful when confirming the plan using a conventional brachytherapy dose computation method, and moreover, an independent dose verification system as developed in this study might be helpful.
Accounting ; Brachytherapy ; Film Dosimetry ; Humans ; Monte Carlo Method ; Organoplatinum Compounds ; Population Characteristics ; Uterine Cervical Neoplasms ; Water

OBJECTIVE: To simulate the B₁-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: B₁-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R₁, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (K(trans), v(e), and v(p)). RESULTS: B₁-inhomogeneity resulted in −23–5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: −16.7–61.8% and −16.7–61.8% for the pre-contrast R₁, −1.0–0.3% and −5.2–1.3% for the contrast-enhancement ratio, and −14.2–58.1% and −14.1–57.8% for the Gd-concentration, respectively. These resulted in −43.1–48.4% error for K(trans), −32.3–48.6% error for the v(e), and −43.2–48.6% error for v(p). The pre-contrast R₁ was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a −10% FA error led to a 23.6% deviation in the pre-contrast R₁, −0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a −10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were −23.7% for K(trans), −23.7% for v(e), and −23.7% for v(p). CONCLUSION: Even a small degree of B₁-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R₁ calculations to FA deviations is a significant cause of the miscalculation.
Brain ; Gray Matter ; Magnetic Resonance Imaging* ; Monte Carlo Method ; Phantoms, Imaging