Modeling scattered radiation from dose compensator in CT by forced detection Monte Carlo simulation

Purpose: To investigate dose distributions in the prostate and surrounding tissues for I–125 brachytherapy using Monte Carlo (MC) calculations with patient‐specific phantoms derived from CT images. We explore the effectiveness of different techniques to mitigate streaking artifacts due to brachytherapy sources in post‐implant CT images. Methods: Nine patients (45–87 sources) are considered. Streaking artifacts in post‐implant CT images are mitigated using various metallic artifact reduction (MAR) techniques: raw sinogram, fan beam virtual sinogram and 3D median filter. Segmented structures (CTV, rectum and bladder) guide the assignment of tissues (air, muscle, prostate, calcification, average tissue and bone) to develop patient‐specific MC phantoms. The EGSnrc user‐code Brachydose and GEANT4 user‐code ALGEBRA are employed for MC dose calculations using patient‐specific phantoms derived from CT images. Dose distributions generated with patient models derived using different MAR techniques and different calculation methods (MC, TG‐43) are compared directly, and using recommended dose metrics. Results: Application of each MAR technique to patient CT data results in comparable mitigation of streaking artifacts within the treatment volume in CT images. MC calculations based on uncorrected CT data Result in high dose spikes (>200%) in the treatment volume and lower doses (40–70%) in surrounding tissues compared to MC calculations with corrected phantoms. Dose distributions within the prostate from MC simulations using phantoms generated with different MAR techniques are comparable to each other but differ from TG‐43 calculations with significant (10%+) local differences. Conclusion: Mitigation of streaking artifacts in CT images is necessary for patient‐specific MC dose calculations; however, dose distributions and clinical metrics in the target and OAR are insensitive to the particular MAR technique applied. Differences between MC and TG‐43 dose distributions within the target volume and surrounding organs for I‐125 prostate brachytherapy underline the importance of patient‐specific model‐based dose calculations for treatment planning and evaluation. NSERC, CCSRI, The Canada Research Chairs Program

With the advent of powerful computers the use of Monte Carlo (MC) techniques for radiation transport simulation has become one of the most popular methods for modeling X-ray computed tomography (CT) scanners. One significant problem in the use of MC calculations is the presence of statistical uncertainties in the estimates. A simple but not practical way to decrease statistical uncertainties is to run MC simulations for sufficiently long time (large number of histories) and use efficient variance reduction techniques. In this paper, we propose a hybrid approach combining MC and analytical simulations to speed up X-ray CT modeling. In addition, an analytic method for modeling of beam hardening effect (BHE) in CT is proposed for fast generation of raw data for use in validation of BHE correction algorithms. In this method, the contribution of the primary component to the projections is calculated through analytical simulations using ray-tracing methods whereas the contribution of the scatter component is calculated using pure MC simulations. The final projections are determined by appropriate combination of both simulation results. The hybrid simulator and analytic BHE modelling were validated through comparison with experimental measurements and pure MC simulation of different phantoms performed on clinical CT scanners. In general, there is good agreement between the simulated and measured projections and reconstructed images. This work has demonstrated the feasibility of using the hybrid approach for fast and accurate modeling of CT scanners thus allowing to evaluate the effect of physical, geometrical and other design parameters on CT scanner performance and image quality. The results also validate the proposed method for analytical modeling of BHE which is a useful tool for fast generation of raw CT datasets.

Thermal-pulse method is a powerful tool for measuring space charge distributions in polymer films. The data analysis for thermal-pulse method involves the Fredholm integral equation of the first kind, which requires an appropriate numerical procedure to obtain a solution. Various numerical techniques, including scale transformation and regulation method, are proposed. Of those numerical methods, the scale transformation (ST) is the simplest and the most widely used method. However, it presents a high spatial resolution only near the sample surface. Monte Carlo (MC) method is one of the recently proposed ways to solve the equation numerically and has been successfully applied to the analysis of laser intensity modulation method data, which also involves the Fredholm integral equation of the first kind. In this paper we attempt to analyze thermal-pulse data in frequency domain with the MC method and discuss its effectiveness based on some numerical simulations. The simulation results indicate that the electric field profiles can be effectively extracted by the MC method. The computed profiles by the MC method consist well with the supposed distributions in the entire thickness of the sample, while the profiles reconstructed by the ST method fit very well to the supposed one at the vicinity of the target surface and distort sharply along the direction of the thermal pulse propagation in the sample bulk. On the other hand, the oscillations in the computed results by the MC method could deteriorate its accuracy in this study. The influence of noise level on the analysis based on the MC method is also tested by the use of the simulated data. The results show that the computed profiles would become more fluctuant as the noise level increases. This problem can be solved by selecting a larger value of tolerance during the singular value decomposition procedure. Thus, the value of tolerance is considered to be one of the key parameters in this algorithm, which is actually hard to determine. Additionally, the experimental data obtained from a polypropylene film under applied electric field are analyzed to illustrate the feasibility of MC method to be applied to the thermal-pulse experimental data. The results also show that the spatial accuracy by the MC method in the entire sample thickness is higher than by the ST method, which verifies that the MC method is more suitable for detecting the electric field distribution in the deep bulk of the sample. Owing to noise and error, the accuracy of MC calculation depends on the chosen tolerance value, which is now considered to be an obstacle in applying this method to the practical thermal-pulse measurement.

Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated. Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from -21.8 to -0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction. The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

<abstract> A bidirectional Monte Carlo (BDMC) method based on reversibility of bundle trajectory and reciprocity of thermal radiative energy exchange was developed to solve radiative heat transfer in absorbing and scattering medium. Two types of sampling models were introduced into the Monte Carlo (MC) simulation, namely the equivalent sampling and the weight sampling, respectively. Mathematical formula for the sampling models and the statistical calculation of sampling bundles were derived. Furthermore, the reciprocity error correlation of radiative exchange factors between the BDMC method and the traditional Monte Carlo (TMC) method were demonstrated and analyzed. Radiative heat transfer in a two-dimensional rectangular domain with absorbing and scattering media was solved by using both the BDMC method and the TMC method. Radiative exchange factors and radiative equilibrium temperature profiles predicted by the BDMC method were compared with those predicted by the TMC method. The performance parameter P, defined to evaluate the performance of MC methods, was computed and compared between the BDMC and the TMC methods. The results showed the superiority of BDMC method compared with the TMC method for radiative heat transfer, in addition, the weight sampling was proved to be more flexible than the equivalent sampling in the BDMC method. </abstract>

In this work Monte Carlo (MC) simulations are used to correct kilovoltage (kV) cone-beam computed tomographic (CBCT) projections for scatter radiation. All images were acquired using a kV CBCT bench-top system composed of an x-ray tube, a rotation stage and a flat-panel imager. The EGSnrc MC code was used to model the system. BEAMnrc was used to model the x-ray tube while a modified version of the DOSXYZnrc program was used to transport the particles through various phantoms and score phase space files with identified scattered and primary particles. An analytical program was used to read the phase space files and produce image files. The scatter correction was implemented by subtracting Monte Carlo predicted scatter distribution from measured projection images; these projection images were then reconstructed. Corrected reconstructions showed an important improvement in image quality. Several approaches to reduce the simulation time were tested. To reduce the number of simulated scatter projections, the effect of varying the projection angle on the scatter distribution was evaluated for different geometries. It was found that the scatter distribution does not vary significantly over a 30-degree interval for the geometries tested. It was also established that increasing the size of the voxels in the voxelized phantom does not affect the scatter distribution but reduces the simulation time. Different techniques to smooth the scatter distribution were also investigated.

Influence of X-ray scatter radiation on image quality in Digital Breast Tomosynthesis (DBT)

Background Small megavoltage photon fields are increasingly used in modern radiotherapy techniques such as stereotactic radiotherapy. Therefore, it is important to study the reliability of dosimetry in the small-field conditions. The IBA Razor Nano Chamber (Nano chamber) ionization chamber is particularly intended for small-field measurements. In this work, properties of the Nano chamber were studied with both measurements and Monte Carlo (MC) simulations. Material and methods The measurements and MC simulations were performed with 6 MV, 6 MV FFF and 10 MV FFF photon beams from the Varian TrueBeam linear accelerator. The source-to-surface distance was fixed at 100 cm. The measurements and MC simulations included profiles, percentage depth doses (PDD), and output factors (OF) in square jaw-collimated fields. The MC simulations were performed with the EGSnrc software system in a large water phantom. Results The measured profiles and PDDs obtained with the Nano chamber were compared against IBA Razor Diode, PTW microDiamond and the PTW Semiflex ionization chamber. These results indicate that the Nano chamber is a high-resolution detector and thus suitable for small field profile measurements down to field sizes 2 × 2 cm2 and appropriate for the PDD measurements. The field output correction factors and field OFs were determined according to TRS-483 protocol In the 6 MV FF and FFF beams, the determined correction factors were within 1.2% for the field sizes of 1 × 1 cm2–3 × 3 cm2 and the experimental and MC defined field output factors showed good agreement. Conclusion The Nano chamber with its small cavity volume is a potential detector for the small-field dosimetry. In this study, the properties of this detector were characterized with measurements and MC simulations. The determined correction factors are novel results for the NC in the TrueBeam fields.

Objective: In situations where computed tomography (CT) scans are necessary for pregnant patients, such as in cases of trauma or other conditions that provide clinical benefit, accurately estimating the radiation dose to which the fetus is exposed is crucial. However, current methods are not sufficiently practical or feasible for routine clinical use. This study aims to assess and calculate the fetal dose and associated organ doses resulting from CT scans in pregnant patients using the Monte Carlo Simulation Method. Methods: Monte Carlo (MC) simulations and related calculations were conducted on pregnant patient phantoms for different gestational periods (8-15 weeks) using a 64-slice CT scanner (Discovery CT750 HD GE Healthcare) to estimate fetal doses. Organ dose calculations were also carried out. The MC code simulating dose distributions was validated with measurements from the CT Dose Index (CTDI) following AAPM protocols. Volumetric CTDI values from the MC simulations were normalized, enabling the development of a calculation algorithm for fetal dose assessments across different body regions and exposure settings. The algorithm, approved by the institutional review board, was validated through patient-specific MC simulations on CT data from pregnant patients (with gestational ages of 8-15 weeks) who had undergone CT scans. Results: Based on the data, it can be concluded that the fetus was exposed to low radiation doses, which do not present significant risk to fetal development. The doses observed in the study fell within acceptable clinical limits, indicating that fetal radiation exposure during CT scans can be managed safely with appropriate protocols. However, minimizing radiation exposure during pregnancy is essential. The use of low-dose protocols, as demonstrated in this study, is especially important for these patients. Moreover, the study’s findings highlight the importance of optimizing CT scan parameters and adopting radiation reduction strategies in routine clinical practice. Alternative imaging methods that utilize non-ionizing radiation, such as ultrasound or MRI, should also be considered when clinically appropriate. Conclusions: This study provides a clinically applicable approach to calculating fetal radiation doses during CT scans. The developed algorithm can help reduce fetal exposure and ensure patient safety. Future studies could expand on this research by validating the algorithm in larger patient cohorts and different gestational stages. The findings emphasize the need for radiology technologists to use the lowest possible dose protocols and explore non-ionizing imaging alternatives whenever feasible. .

In these days, Monte Carlo (MC) simulation is a method that can calculate the radiation dose that occurs in an environment in the most accurate way. The correct measurement of the dose occurring on the patient’s surface is of great importance to estimate the reactions that may occur on the patient’s skin. This importance encouraged us to do this study. The aim of this study is to determine buildup region and surface doses using MC simulation and to compare them with results of the parallel plane ion chamber and Treatment Planning System (TPS) measurements for 6-MV photon beams. Surface doses normalized to the maximum dose for the parallel plane ion chamber, MC simulation, fast photon (FP) algorithm, and collapsed cone convolution superposition (CC) algorithm are 13.6%, 30.28%, 0%, and 27.33%, respectively. The CC algorithm and parallel plane ion chamber measurements are compatible with MC simulation but the FP algorithm has calculated the dose less to a depth of 0.8 cm. Measuring the surface dose and the doses in the buildup region is of great importance in terms of accurately predicting the complications that may occur in the patient’s skin and taking precautions early. Using some methods and correction factors, the surface dose and the doses that may occur in the buildup region can be accurately calculated. It is recommended not to use the FP algorithm for stereotactic body radiation therapy and intensity-modulated radiation therapy treatments, as it cannot calculate doses correctly in the buildup region and surface.

Purpose: To create a Monte Carlo (MC) model of an ocular proton therapy device, or ocular nozzle, that can predict relative doses to within 3% or 0.5 mm, D/MU values, and be used to develop and commission an analytical algorithm to predict doses and D/MU values to within similar criteria. Methods and Materials: Depth dose profiles and D/MU values were measured in eight ocular proton therapy fields including four unmodulated and four modulated beams using a parallel‐plane ionization chamber in water. Crossfield profiles were measured at three or more depths for each of the modulated beams using film. A MC model of the ocular nozzle was constructed using the MCNPX radiation transport code. After benchmark tests against the measurements, the MC model was used to provide dosimetric data needed to develop a broad‐beam algorithm capable of predicting relative and absolute doses and to test the algorithm's predictions more comprehensively than could be afforded with measured data. Specifically, MC and analytical predictions were calculated and compared in two‐dimensions in a water phantom and in an anthropomorphic model of the eye. Results: Relative depth dose profiles between MC and measurements agreed to within 3% or 0.5 mm. MC predicted D/MU values agreed to within 1% of the measured values. Similarly, the analytical algorithm accurately predicted dose distributions where the predictions were generally within 3% or 0.5 mm of measured and simulated values at water depth &gt; 6 mm. At shallower depths, the analytical model underestimated the dose by approximately 3% to 6%, which MC simulations revealed was due to edge‐scattered protons from the nozzle's collimators. Conclusion: The MC model accurately predicted dose distributions and D/MU values in ocular proton therapy beams. Furthermore, the present work demonstrates the value of the MC method for developing and testing contemporary analytical dose algorithms.

As energy demand increases and global warming progresses, expectations for fuel cells, which generate electricity through chemical reactions between hydrogen and oxygen, are rising. There are various types of fuel cells, which are classified according to the electrolyte. Polymer electrolyte fuel cells (PEFCs) are expected to be used in stationary power sources for home use and fuel cell vehicles because of their low operating temperature, short start-up time, and ease of miniaturization. The cost has been an issue in the widespread use of fuel cells. The amount of platinum used in the catalyst layer (CL) must be reduced to reduce costs. However, it increases the current density per unit area of platinum. There are three factors that contribute to voltage drop: ohmic polarization, activation polarization, and diffusion polarization. At high current densities, the voltage drop due to diffusion polarization is dominant. The main cause of diffusion polarization is the transport resistance of oxygen molecules in the CL, and improving the oxygen transport properties in the CL will lead to lower cost PEFCs. In this study, we analyzed the transport properties of oxygen molecules in the CLs of PEFCs using the Monte Carlo (MC) method. The effect of oxygen scattering on the overall oxygen transport properties was investigated by considering the behavior on the surface of the ionomer film (surface diffusion) into the MC method, which simulates the overall transport in the PEFC CL. The objective of this study is to reproduce accurate oxygen transport by comparing the results of MC and molecular dynamics (MD) methods. First, we used the MC method to analyze oxygen scattering phenomena. In this study, we used the 3D structure data of CL in the MC simulations. The three-dimensional structure of the sample was reconstructed from sequential slice images. Molecules were assumed to reflect specularly on the boundaries of the simulation domain. Oxygen molecules were randomly placed in the simulation system. The time step was set at 5 ps and the simulation time was set at 4 μs. In order to examine the effect of surface diffusion on the transport properties of oxygen molecules, the simulation was performed using various conditions of surface diffusion. We calculated the effective diffusion coefficient of oxygen in the CL as the transport property of oxygen molecules in the MC method. As the surface diffusion coefficient increases, the effective diffusion coefficient of oxygen increases. When the surface diffusion coefficient is small, the effective diffusion coefficient of oxygen increases due to a decrease in the time constant, which determines the probability distribution of the surface residence time. However, when the surface diffusion coefficient is large, a different trend emerges. As the time constant increases, the effective diffusion coefficient has a peak value and then decreases. Next, to verify the model of surface diffusion in the MC method, we analyzed the scattering phenomenon of oxygen molecules in a simulation system that simulates the pores in the CL using the MD method. We modeled the nano pores in the CL as a slit between the ionomer walls in the MD method. Nafion with the equivalent weight (EW) of 1146 was used as polymer model. The number of Nafion chains is set at 4, and the water content λ is set at 7. Three-dimensional periodic boundary conditions were applied to the simulation domain. After the system was equilibrated, 1 ns of NVT simulation was performed for production. The temperature was set at 297 K. The behavior of oxygen molecules impacting on the ionomer wall was analyzed. We analyzed the behavior of oxygen molecules colliding with the ionomer membrane using the MD method. The average residence time of surface diffusion on the ionomer thin film was of the order of 10-10s. When the calculation results were compared with the MC results, it was found that the average residence time obtained by the MD method was the value where the surface diffusion coefficient affects much on the effective diffusion coefficient. Comparing the results of the MD method with the surface diffusion model used in the MC method, there are some discrepancies between the results and the model. For example, the MC method used a model in which oxygen molecules reflect diffusively on the ionomer surface. However, in the MD method, it was found that oxygen molecules tend to reflect in the direction of travel (Fig. 1). Therefore, a more accurate model of the MC method needs to be developed in the future. Figure 1

We combine molecular dynamics (MD) and Monte Carlo (MC) simulations to study the properties of gas molecules confined between two hard walls of a microchannel or nanochannel. The coupling between MD and MC simulations is introduced by performing MD near the boundaries for accuracy and MC in the bulk because of the low computational cost. We characterize the influence of different densities and molecule sizes on the equilibrium properties of the gas in the microchannel. The effect of the particle size on the simulation results is very small in the case of a dilute gas and increases with the density. The hybrid MD-MC simulation method is validated by comparing the results for density and temperature profiles with those of pure MD and pure MC simulations. These results compare well for pure MD and pure MC, as well as hybrid MD-MC, both in the bulk and near the boundaries, when hard-sphere interactions are used. When Lennard-Jones potentials are used to accurately model the interactions between the gas and wall molecules instead, the results of pure MD simulations differ significantly from the pure MC simulations near the boundaries, but the results of the hybrid method compare well with the pure MD results near the wall, and with the pure MC and pure MD results in the middle of the channel. The hybrid method also very accurately simulates the interface between the MD and MC simulation domains. Comparisons between MD, MC, and hybrid MD-MC computational costs are outlined. The speedup when using 50% of the domain for MD simulations and 50% for MC simulations is very small compared to pure MD simulations times, but this speedup increases drastically for more realistic situations where the region near the wall is small compared to the bulk region.

Purpose: For microdosimetric calculation event-by-event Monte Carlo (MC) simulation is considered the most accurate, but it is very time-consuming. In this work we present an event-by-event MC simulation of low energy electron and proton for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Methods: The MC simulation of particle was implemented in C and executed on a multi-core CPU, and a commercially available general purpose GPU using the compute unified device architecture (CUDA). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA so that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC simulation on the CPU. A calculation time comparison was established on the speed-up for a set of benchmarking cases of electron and proton. Results: Dosimetric results were obtained with both the parallel and serial MC codes. A GPU over CPU speed-up of 67.2 and 19.2 times was achieved for 300 eV and 2 keV electron tracks respectively. For proton tracks, the GPU-based code was approximately 5 times faster than the CPU-single-thread code. By incorporating a multi-core CPU and running the MC code simultaneously on the GPU and CPU, an increase of 2%-7% and 20% in the speedup was noticed for electrons and protons tracks respectively. A good dosimetric agreement between the CPU- and GPU-based MC methods was observed for both electrons and proton. Conclusions: A GPU-based MC method for microdosimetric calculations of low energy electron and proton has been presented. The results indicate the capability of our GPU-based implementation for accelerated MC simulations of both electron and proton without loss of accuracy. Lastly, the potential of a hybrid approach by utilizing simultaneously a GPU and a multi-core CPU for further acceleration of MC microdosimetric calculations has been demonstrated.

Objective. Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used deep learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, graphics processing unit limitations constrained these predictions to large voxels of 3 mm × 3 mm × 3 mm. This study aimed to enable dose predictions as accurate as MC simulations in 1 mm × 1 mm × 1 mm voxels within a clinically acceptable timeframe. Approach. Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (D m,m ). These architectures fuse information from TG-43 dose to water-in-water (D w,w ) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results. The proposed approach demonstrated state-of-the-art performance, on par with the MC D m,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17% ± 0.15% for the planning target volume V 100, 0.30% ± 0.32% for the skin D 2cc , 0.82% ± 0.79% for the lung D 2cc , 0.34% ± 0.29% for the chest wall D 2cc and 1.08% ± 0.98% for the heart D 2cc . Significance. Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 D w,w maps into precise D m,m maps at high resolution, enabling clinical integration.