Monte Carlo Simulations Research Articles

Comparing Ridge Regression Estimators: Exploring Both New and Old Methods

Abstract Ridge regression presents a method to tackle multicollinearity issues. Several estimators and predictors for the estimation of biasing parameter k have been extensively detailed in scholarly literature. We offer a thorough analysis of both conventional and emerging methods aimed at precisely determining the ridge parameter k. Our investigation provides valuable insights into the properties of these estimators and their practical efficacy in various applications. Proposed estimators for the parameter k are assessed using Monte Carlo simulations and a real-world example, with a focus on evaluating their performance based on Mean Squared Error (MSE). Our estimator, in conjunction with others, showcases commendable performance, as indicated by the results.

Classical and Bayesian Estimation of PCI 𝒞pc Using Power Generalized Weibull Distribution

Abstract This article focuses on estimating the process capacity index (PCI), 𝒞 pc \mathcal{C}_{\rm pc} , where the underlying distribution is a power generalized Weibull distribution, using maximum likelihood and Bayesian techniques. The 𝒞 pc \mathcal{C}_{\rm pc} index may be used to both normal and non-normal quality attributes. This article aims to accomplish three things: Using the maximum likelihood approach, we first determine the estimator of the PCI 𝒞 pc \mathcal{C}_{\rm pc} . Secondly, the Metropolis-Hastings Algorithm is used to study Bayesian estimation under four loss functions (symmetric and asymmetric). Third, the index 𝒞 pc \mathcal{C}_{\rm pc} ’s 95 confidence intervals are built using Bayesian and four bootstrap techniques. The point estimates of 𝒞 pc \mathcal{C}_{\rm pc} have been evaluated using Monte Carlo simulation in terms of mean square errors (MSEs), four bootstrap methods, and highest posterior density (HPD) credible intervals in relation to their average width (AW) and coverage probabilities (CPs). Using two real data sets – one pertaining to the size of electrical connections and the other to the amount of protein (in g) for adult patients at the Hospital Carlos Van Buren in Valparaiso, Chile – a comparable analysis to that employed in the simulations is conducted in order to illustrate the effectiveness of the suggested methods.

A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.

Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated microbiome studies. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To study the microbiome co-occurrence network and perform community detection, we propose a generalized Bayesian stochastic block model that is tailored for microbiome data analysis where the data are transformed using the recently developed modified centered-log ratio transformation. Our model also allows us to leverage taxonomic tree information using a Markov random field prior. The model parameters are jointly inferred by using Markov chain Monte Carlo sampling techniques. Our simulation study showed that the proposed approach performs better than competing methods even when taxonomic tree information is non-informative. We applied our approach to a real urinary microbiome dataset from postmenopausal women. To the best of our knowledge, this is the first time the urinary microbiome co-occurrence network structure in postmenopausal women has been studied. In summary, this statistical methodology provides a new tool for facilitating advanced microbiome studies.

Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP

Abstract To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. The performance of supervised DNNs (SVNN), self-supervised physics-informed DNNs (PINN), and an iterative fitting framework termed motion-insensitive rapid configuration relaxometry (MIRACLE) was evaluated in silico and in vivo in brain tissue of healthy subjects, including Monte Carlo sampling to simulate noise. DNNs were trained on three distinct in silico parameter distributions and at different signal-to-noise-ratios. The PINN framework, which incorporates physical knowledge into the training process, ensured more consistent inference and increased robustness to training data distribution compared to the SVNN. Furthermore, DNNs utilizing the full information of the underlying complex-valued MR data demonstrated ability to accelerate the data acquisition by a factor of 3. Whole-brain relaxometry using DNNs proved to be effective and adaptive, suggesting the potential for low-cost DNN retraining. This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.

Propagation of systematic sensor uncertainty into the frequency domain

Abstract When it comes to the identification of dynamic system parameters, like stiffness and damping, the systematic measurement uncertainty is mostly ignored in the frequency domain because of its complicated and elaborate uncertainty propagation. Nevertheless, the uncertainty is far from being negligible. In order to examine the importance of the systematic measurement uncertainty in the frequency domain, the propagation of systematic uncertainty caused by four different sensor error types is investigated by using Monte Carlo simulations. It is shown that an uncertainty in the bias only affects static measurements, i.e. with an excitation frequency of the system being equal to 0. Furthermore, the uncertainty of the linearity and sensitivity only impacts the magnitude of the Fourier coefficient, whereas the uncertainty in the hysteresis results only in an uncertainty in the phase. Subsequently, it is shown that the linearity and hysteresis uncertainty are completely independent in the frequency domain. The results lead to simple propagation relations applicable to various engineering, science and industry challenges without the need for the conduction of complex Monte Carlo simulations.

Probabilistic assessment of passive earth pressures considering spatial variability of soil parameters and design factors

The stability design of soil structures, such as retaining walls, has traditionally relied on deterministic analyses that use averaged soil parameters. However, modern computational advances, particularly Monte Carlo simulations (MCs), have highlighted the limitations of this approach. Engineers are now increasingly focused on understanding the probability of failure (PF) associated with specific safety factors. This study explores the impact of spatial variability in soil properties, specifically the friction angle and unit weight, on PF. By applying log-normal distributions and spatial correlation lengths, extensive MC simulations were combined with finite element limit analysis to establish relationships between safety factors and PF. This research makes several contributions to geotechnical engineering. It integrates MC simulations with finite element limit analysis to provide a statistically robust framework for evaluating PF in soil structures. The use of adaptive finite element meshes introduces new insights into failure mechanisms, addressing gaps in existing studies. Additionally, the study presents a set of parametric design charts that enable practitioners to estimate PF for specific safety factors effectively. These findings provide practical tools for design engineers, supporting better decision-making and increasing confidence in design outcomes.

Developing Novel Lattice Mapping for Accurate and Efficient Charge Transport Modeling from Atomistic Morphology.

This study focuses on numerical methods to compute charge carrier mobility in disordered materials, such as organic light-emitting diodes (OLEDs), based solely on molecular structures. The approach involves developing an ab initio method for calculating charge carrier mobility in organic materials using kinetic Monte Carlo (KMC) simulations. These simulations utilize Marcus rates derived from precise calculations of transfer integrals and site energies specific to the material's morphology. Going beyond the current approach to tackle a multicharge model system presents computational challenges, particularly in calculating site energies. To address this issue, a novel lattice mapping method was developed to efficiently determine transfer integrals and site energies from realistic morphologies while keeping computational costs manageable. Validation of the method was conducted by comparing mobility values computed using the KMC method with experimental data, showing a good agreement. Further insights into charge transport dynamics were gained through the analysis of charge carrier behavior using residence time calculations. Additionally, the model's applicability to multicharge systems was demonstrated by simulating exciton formation. In conclusion, the model has the potential to effectively and accurately simulate charge carrier trajectories in multicharge, multilayer models with minimal loss of information from realistic morphology, making it a valuable tool for designing and optimizing organic electronic devices.

Evaluation of deep learning-based scatter correction on a long-axial field-of-view PET scanner.

Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However this extended angle increases the number of multiple scatters and the scatter contribution within oblique planes. As scattering affects both quality and quantification of the reconstructed image, it is crucial to correct this effect with more accurate methods than the state-of-the-art single scatter simulation (SSS) that can reach its limits with such an extended field-of-view (FOV). In this work, which is an extension of our previous assessment of deep learning-based scatter estimation (DLSE) carried out on a conventional PET system, we aim to evaluate the DLSE method performance on LAFOV total-body PET. The proposed DLSE method based on an convolutional neural network (CNN) U-Net architecture uses emission and attenuation sinograms to estimate scatter sinogram. The network was trained from Monte-Carlo (MC) simulations of XCAT phantoms [ F]-FDG PET acquisitions using a Siemens Biograph Vision Quadra scanner model, with multiple morphologies and dose distributions. We firstly evaluated the method performance on simulated data in both sinogram and image domain by comparing it to the MC ground truth and SSS scatter sinograms. We then tested the method on seven [ F]-FDG and [ F]-PSMA clinical datasets, and compare it to SSS estimations. DLSE showed superior accuracy on phantom data, greater robustness to patient size and dose variations compared to SSS, and better lesion contrast recovery. It also yielded promising clinical results, improving lesion contrasts in [ F]-FDG datasets and performing consistently with [ F]-PSMA datasets despite no training with [ F]-PSMA. LAFOV PET scatter can be accurately estimated from raw data using the proposed DLSE method.

A practically oriented, efficient alternative to the Monte Carlo Method for measurement uncertainty estimation

A practically oriented, efficient alternative to the Monte Carlo Method for measurement uncertainty estimation

Commissioning small fields in lung using Monte Carlo corrected film measurements.

Current commissioning of radiotherapy treatment planning systems with heterogeneous phantoms generally measures the dose delivered in solid water downstream of the lung material. The dose delivered directly in lung material is important to characterise the uncertainty of lung stereotactic body radiotherapy (SBRT) treatments, but film measurements require a correction to accurately measure dose to lung. Monte Carlo (MC) modelled corrections were applied to film measurements used for commissioning of lung SBRT. Medium dependent correction factors, kmed, were established using 6 and 10 MV simulations to account for film being calibrated in water but measuring in regions of lung material. The correction factors are dependent on energy, field size, and position. To avoid the onerous requirement of modelling each individual beam to correctly match penumbra an alternative approach is presented where the correction is applied as a function of isodose level for a nominal field size. Improvement in central axis dose agreements was seen for all field sizes, with the largest improvement of 7% observed for 6MV 1×1cm2 field. Application of position dependent corrections improved the percentage of points passing a 5%/1mm or 3%/1mm gamma assessment in all cases, whilst a uniform central axis correction did not improve the passing rate in most cases. MC simulations provide a method for correcting dose measured in lung materials allowing more accurate comparison with treatment planning system doses. In this work a generic approach to correct small field film lung measurements as a function of isodose levels is presented.

Beam collimation and filtration optimization for a novel orthovoltage radiotherapy system.

The inaccessibility of clinical linear accelerators in low- and middle-income countries creates a need for low-cost alternatives. Kilovoltage (kV)x-ray tubes have shown promise as a source that could meet this need. However, performing radiotherapy with a kV x-ray tube has numerous difficulties, including high skin dose, rapid dose fall-off, and low dose rates. These limitations create a need for highly effective beam collimation andfiltration. To improve the treatment potential of a novel kV x-ray system by optimizing an iris collimator and beam filtration using Bayesian techniques and Monte Carlo (MC)simulations. The Kilovoltage Optimized AcceLerated Adaptive therapy system's current beam configuration consists of a 225kVp x-ray tube, a 12-leaflet tungsten iris collimator, and a 0.1mm copper filter. A Bayesian optimization was performed for the large and small focal spot sizes of the kV x-ray tube source at 220kVp using TopasOpt, an open-source library for optimization in TOPAS. Collimator thickness, copper filter thickness, source-to-collimator distance (SCD), and source-to-surface distance (SSD) were the variables considered in the optimization. The objective function was designed to maximize the dose rate and the dose at a depth of 5 cm while minimizing the beam penumbra width and the out-of-field dose (OFD), all evaluated in a water phantom. Post-optimization, the optimal beam configuration was simulated and compared to the existingconfiguration. The optimal collimation setup consisted of 2.5mm thick tungsten leaflets for the iris collimator and a 350mm SSD for both focal spot sizes. The optimal copper filtration was 0.22mm for the large focal spot and 0.15mm for the small focal spot, with a SCD of 148.5mm for the large focal spot and 125.8mm for the small focal spot. For the large focal spot, the surface dose rate decreased by 9.4%, while the PDD at 5cm depth ( ) increased by 7.7% compared to the existing iris collimator. Additionally, the surface beam penumbra width was reduced by 31.3%, and no significant changes in the OFD were observed. For the small focal spot, the surface dose rate for the new collimator increased by 3.7% and the increased by 5.3%, with no statistically significant changes in the beam penumbra width orOFD. The optimal beam collimation and filtration for both x-ray tube focal spot sizes of a kV radiotherapy system was determined using Bayesian optimization and MC simulations and resulted in improved dosedistributions.

Spatial resolution improvements for photon counting detectors using coincidence counting and frequency weighted reconstruction.

Photon counting X-ray detectors (PCDs) provide better spatial resolution than energy integrating X-ray detectors, but even higher resolution is desired in some applications. Certain charge sharing compensation techniques such as coincidence counting preferentially detect photons that arrive at the boundary between pixels, and this could be used for subpixel localization of incident photons and enhanced spatial resolution. To estimate improvements to spatial resolution and detective quantum efficiency that are possible when using coincidence counting for high-resolution, non-spectral imaging. The modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) were estimated using numerical simulations of a two-dimensional parallel-beam CT system. Coincidence counters were modeled either geometrically or with Monte Carlo simulations. The geometric model consisted of narrow coincidence counters that interlaced with wider ordinary pixels, and showed that using standard filtered backprojection decreases low-frequency DQE, but that a frequency weighting technique could be used to restore DQE. The Monte Carlo simulations were used to estimate the possible improvements that could be expected from real systems. The pixel pitch was 0.25 mm and the source apertures considered were 0, 0.125, and 0.5 mm. A numerical stent phantom was also used to illustrate possible improvements. Assuming a 0.125 mm source aperture and the Monte Carlo model, the limiting MTF (at 10%) increased from 20 to 40 lp/cm using coincidence counters. This can be explained by the increased sampling (and higher Nyquist limit) possible from coincidence counters. In the geometric model, coincidence counters are compared to conventional double sampling techniques such as in-plane flying focal spot, and the MTF at 38 lp/cm increased from 5% to 53%. Without the frequency weighting technique, low-frequency DQE was reduced by about 20%, but these losses are recovered with frequency weighting. Improvements are much more modest with the 0.25 mm source aperture because the system becomes source-limited. Coincidence counting could be used to increase spatial resolution in PCDs. The increases in system resolution could be large if a high-resolution X-ray source were available.

Ecological and human health risks of potentially toxic elements (PTEs) in street dust of Al-Hillah City, Iraq using Monte Carlo simulation.

Street dust is a primary source of metal pollution in urban environments, posing a significant threat to human health through chronic exposure via inhalation, ingestion, and skin contact. This study used deterministic and Monte Carlo simulation to assess the health risks of potential toxic elements (PTEs) in the street dust of Al-Hillah City. The average concentrations of elements in the samples followed the order: Al>Fe>S>K>Sr>Mn>Cr>Ba > Zn>Ni>Pb>Cu>Co>As > Sn>Sb>Cd. In the study area, all the measured elements exceeded UCC values except for Al, Ba, Fe, and K. The results for the enrichment factor (EF), geo accumulation index (Igeo), and contamination factor (CF) revealed that the most sampled locations were polluted with sulfur (S), arsenic (As), and chromium (Cr). The highest values of the pollution load index were not for a solely land use class; they were identified at different sampling stations. According to the potential ecological risk rating, As and Cd pose a medium risk, while Cr, Cu, Ni, Pb, and Zn have low risks. The probabilistic Monte Carlo simulation highlighted the significant health risks from PTEs in street dust, especially for children, with HI values of 2.01, 3.24, and 5.26 at the 5th, 50th, and 95th percentiles, respectively. In comparison, HI values for adults were much lower at 0.29, 0.41, and 0.58, remaining within safe limits. Lifetime Cancer Risk (LTCR) estimates showed that 99.7% of adults and 97% of children exposed to levels exceeding the safe threshold 1E-4. Sensitivity analysis revealed that chromium (Cr) and nickel (Ni) were the main PTEs contributing to health risks in children and adults' groups.

Monte Carlo simulations of backscattered electron coefficients and average penetration depths for Cu, Au, and Al under electron irradiation

Understanding and analyzing backscattered electron coefficients (BSCs) and average penetration depths (APDs) are critical in material science and electron microscopy. However, despite their importance, there are relatively few studies that cover a wide range of energies and a variety of materials for accurately calculating BSCs and APDs. Therefore, a Monte Carlo (MC) simulation was conducted to examine and explore the electron backscattering coefficients and average penetration depths of copper (Cu), gold (Au), and aluminum (Al) when bombarded by energetic electrons with energies ranging from 0 to 60 kilo-electron volts (keV) at normal incidence. The results showed strong agreement with experimental data. First, for the BSCs, we have deviations ranging from 0.3 to 5.4%. Second, the empirical calibration adjustment resulted in an excellent agreement with the experimental data of APDs. For Cu, the deviation was 3.32% at 5 keV. The exceptional agreement was observed at 9 keV for Au, with a deviation of just 0.08%. In the case of Al, the adjustment achieved a strong agreement with a deviation of 2.01%. These findings improve our understanding of backscattered electrons behavior by providing accurate simulations for Cu, Au, and Al across a wide energy range, resolving discrepancies, especially for low-Z materials such as aluminum (Z = 13). The improved accuracy in predicting APD and the original BSC results support scanning electron microscopy (SEM) applications, particularly in compositional and topographic imaging.

Identifying sources and risks of polycyclic aromatic hydrocarbons in the Yarlung Tsangpo River: A seasonal study.

The presence of polycyclic aromatic hydrocarbons (PAHs) in the Yarlung Tsangpo River (YTR) poses substantial threats to both regional ecological security and human health. While it is imperative to accurately assess and pinpoint the risks and sources of PAHs in the YTR, a comprehensive understanding of their seasonal variations in composition and distribution is currently lacking. In a thorough investigation aimed at evaluating seasonal fluctuations, sources, potential hazards, and the impact of natural and anthropogenic factors on PAHs concentrations in the YTR, enlightening findings emerged. During the dry season, concentrations of the sum of 16 PAHs (Ʃ16PAHs) ranged from 18.74 to 60.01 ng/L, with low molecular weight PAHs (2-3 rings) prevailing, primarily originating from biomass combustion. Conversely, in the wet season, concentrations varied from 10.35 to 27.04 ng/L, with high molecular weight PAHs (5-6 rings) dominant, largely sourced from transportation emissions. The increased rainfall during the wet season led to a dilution of PAHs concentrations, resulting in lower levels compared to the dry season. Factors such as population density, energy consumption, and energy production exhibited positive correlations with PAHs levels, influencing their spatio-temporal distribution. The spatial distribution of PAHs concentrations was found to be associated with ecological and health risks, with risk quotient analysis indicating lower ecological risks, but heightened risks during in the dry season. Moreover, through monte carlo simulation and risk assessment, it was revealed that there were elevated carcinogenic risks for adults during the dry season. Prioritizing reductions in concentrations of specific PAHs such as DahA and BaP proved effective in mitigating the risk of carcinogenic risk. This study furnishes valuable reference data for the management and regulation of PAHs in the YTR, offering insights crucial for safeguarding the river's ecosystem and the well-being of the communities reliant on it.

Second order sliding mode control with proportional integral observer for wing rock

In this study, a reduced-order fast proportional integral (PI) observer with a fast convergence function based on the equivalent control notion is developed to estimate the side slip angle β. An unknown state can be discovered by forcing the PI term on the state error, which is the difference between the real and estimated states. A second order sliding mode control (SOSMC) based on a proposed nonlinear sliding manifold is designed to achieve improved transient response and control performance by robustness. The proposed sliding manifold ensures the convergence of the roll angle in finite time. To validate the assertion regarding the proposed SOSMC, classical SOSMC and proportional integral derivative (PID) SOSMC are simulated both in the absence and presence of disturbances. The simulation results show that the proposed SOSMC over-performs in terms of achieving the desired response in both cases. Additionally, a numerical analysis is also performed for both scenarios to evaluate the effectiveness of the proposed controller in terms of the power consumption of the control law and burden on the aileron control surface. Finally, Monte Carlo simulations are performed to demonstrate the robustness of the proposed controller against external disturbances.

Data taking strategy for ψ(3770) and ϒ(4S) branching fraction measurements at e+e- colliders

Abstract The $\psi(3770)$ and $\Upsilon(4S)$ states predominantly decay into open-flavor meson pairs, while the decays of $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ and $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ are rare but crucial for elucidating the inner structure and decay dynamics of heavy quarkonium states. To achieve precise branching fraction measurements for the $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ and $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ decays at the high luminosity $e^+e^-$ annihilation experiments, we employed Monte Carlo simulations and Fisher information to evaluate various data taking scenarios, ultimately determining the optimal scheme. The consistent results of both methodologies indicate that the optimal energy points for studies of $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ decays are $3.769~{\rm GeV}$ and $3.781~{\rm GeV}$, while those for $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ decays are $10.574~{\rm GeV}$ and $10.585~{\rm GeV}$. In addition, we provide an analysis of the relationship between the integrated luminosity and the precision in branching fraction measurements derived from these data samples, with the branching fraction spanning several orders of magnitude. In addition, we studied the dependence of the precision in branching
fraction measurements on the integrated luminosity, with the branching fractions spanning several
orders of magnitude.

Impacts of Compression on the Ground and Low-Lying Excited Doublet States of Plasma-Embedded Lithium Atom

The variational Monte Carlo method is employed to conduct a comprehensive investigation of the compressed ground and excited states of plasma-embedded lithium atom within impenetrable spherical boxes of varying radii. The study focuses on the low-lying excited doublet states 1s2ns, 1s2np, and 1s2nd, utilizing plasma potentials such as the screened Coulomb (SCP), exponential cosine screened Coulomb (ECSCP), and Hulthén potentials. Energy eigenvalues are determined using appropriate trial wave functions, which account for electron–electron repulsion and spin parts to adhere to the Pauli Exclusion Principle. Moreover, two factors related to the wave function of the compressed system and ECSCP model are considered. The results reveal an intriguing relative ordering for the lithium atom using the three plasma models, with many of the findings being significant contributions yet to be explored.

Shannon Entropy in Uncertainty Quantification for the Physical Effective Parameter Computations of Some Nanofluids

The main aim of this study is probabilistic computer simulation of the effective physical parameters of fluids containing nanoparticles. A deterministic model following the rule of mixtures and some semi-empirical formulas are employed to calculate effective density, heat conductivity, heat capacity, as well as viscosity for the given nanofluid. This models is randomized here using the Monte-Carlo simulation apparatus for estimation of the Shannon entropy of all these physical parameters, which is the crucial novelty of this study. The volume fraction of the nanoparticles is assumed for this purpose as the Gaussian uncertainty source with the given first two moments. The basic probabilistic characteristics of the nanofluids’ homogenized parameters have also been determined here for some validation of Shannon entropy variations in addition to the statistical disorder of the nanoparticle fraction. These research findings contribute to advancing nanofluidic and microfluidic research, offering robust tools for uncertainty analysis and enhancing the reliability of physical parameter predictions in applications requiring high numerical and/or experimental precision.

Fast and accurate peak skin dose estimation method for interventional fluoroscopy patients.

In fluoroscopy, particularly fluoroscopically guided interventions (FGIs), accurate estimation of peak skin dose (PSD) is crucial for identifying potential radiation-induced skin injuries. Most current methodologies for PSD calculation methods rely on analytical methods, which may introduce uncertainty due to their limited consideration of the complexities of x-ray beam conditions, patient geometry, and positioning. Methods based on full Monte Carlo (MC) simulations can enhance accuracy, but their practical application is limited due to the intensive requirement of computational resources and time. We aimed to develop a novel method that combines MC simulation with a noise reduction technique to calculate PSD, as well as skin dose distributions, more efficiently and accurately. The goal was to overcome the limitations of current methods, providing a more practical solution for clinical and academic use. Our method to calculate the PSD and skin dose distributions consists of two steps of rough MC simulation and iterative noise reduction. The performance of the methodology was demonstrated for six fluoroscopy scenarios, with results compared against those from full MC simulation with high particle history, which is considered a gold standard for radiation dosimetry relative to conventional analytical methods. Our method was demonstrated for various fluoroscopy scenarios, and the result showed that the iterative noise reduction procedure successfully estimates PSD and skin dose distribution for rough MC simulations with a maximum dose statistical error of up to 20%. For successful dose estimations, PSD discrepancies from the values obtained by full MC simulation were within 3%, and voxel-wise dose differences in skin dose distributions were less than 10% of the average skin dose. The computation time of our method was on the order of a few seconds on a personal computer, which is estimated to be at least 104 times faster than full MC simulation when using the same computing resources. Our method rapidly and accurately calculates PSD and skin dose distribution, making it a useful tool for research and clinical applications. The planned integration of our method into the National Cancer Institute Dosimetry System for Radiography and Fluoroscopy (NCIRF) will enhance accessibility. Additionally, future upgrades of NCIRF will include a comprehensive phantom library and pregnant phantoms that will enable our method to account for patient-specific body shapes, further improving the accuracy and personalization in dose assessments.