Photon Transport Simulation Research Articles

Impacts of vertical variations in soil properties on H*(10) simulations for 137Cs deposition

Photon transport simulations based on the Monte Carlo method have played a crucial role in assessing and estimating the ambient dose equivalent rates H*(10), resulting from the deposition of 137Cs in soil following the nuclear power plant accident in Fukushima. However, a comprehensive examination of the effect of vertical variations in soil properties on the simulation outcomes has not yet been performed. Disregarding the vertical distribution of soil properties not only leads to potential inaccuracies in the shielding responses of soil layers but also in the determination of the radioactive source inventory, particularly when using the concentration data in Bq/kg. These oversights diminish the reliability of the simulation results. This study addresses several soil property factors that could potentially influence the simulation results, including variations in chemical composition induced by water content, bulk density profile, and estimated inventory profile, all evaluated through an examined simulation model. The results show that inappropriate assignment of the soil density profile can cause considerable errors in the H*(10) simulation outcomes. Furthermore, the sensitivity of H*(10) to variations in soil vertical density is analyzed, with the results indicating that H*(10) can be highly sensitive to changes in the bulk density of the top 0–5 cm soil layers. These results should facilitate the establishment of appropriate simulation strategies and support the reassessment of past simulation results.

A Generic Implementation of the D1S Methodology for Dose Rate Assessment by Monte Carlo Codes in Fusion Applications

Dose rate assessment is of utmost importance in fusion reactors because of the maintenance operations that these facilities will require. The standard way to perform this assessment in ITER is use of the Direct 1-Step (D1S) methodology, which consists of performing neutron transport simulation, material activation, and decay photon transport simulation in one step and thus in one simulation only. Usually, implementation of the D1S methodology requires changing the source files in a reference Monte Carlo code. The purpose of the present work is to develop an easy-to-implement method for Monte Carlo codes to help calculate the dose rate in fusion reactors. To do so, the proposal is to act on nuclear data only and not on source files of the simulation codes. This is done by replacing prompt photon production in evaluation files by suitable decay photon production, taking into account radioactive decays of radionuclides and irradiation history. This study was to be applied first on the TRIPOLI-4® Monte Carlo code for the sake of simplicity. TRIPOLI-4 is the reference code for particle transport at CEA. The verification and validation process relies first on a comparison to the reference Rigorous 2-Step (R2S) methodology and then on an experiment, the so-called Frascati Neutron Generator (FNG) dose experiment. The nuclear database to be changed is of the ENDF-6 format, a recurrent format in neutronics studies. The analysis studied both neutron and photon responses to check if the simulation was performing normally in a physical way and to compare the results with references provided by simulations based on the R2S methodology or by the FNG dose experiment. The simulations have proven to be in good agreement with the experimental results.

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

Delayed Heating Calculations Using the MCNP-ORIGEN Activation Automation Tool

An accurate assessment of the deposited energy across a reactor geometry allows for a better determination of the heat removal requirements and ensures effective cooling after shutdown. This work discusses several methods in detail that target the heat deposition in specific reactor regions, leading to a choice of a prevalent method for the calculation workflow. This paper introduces a delayed heating calculation workflow based on the formal three-step process. The workflow relies on the MCNP-ORIGEN Activation Automation tool for performing the first two steps of the process, while the third step is conducted via MCNP photon transport simulations. This paper showcases two applications to demonstrate the workflow and simulation outputs. These include an Advanced Test Reactor (ATR) experiment and the RA-6 reactor structures.

A new Excel-based Monte Carlo transport code for simulating energy response spectra and efficiencies in gamma-ray detection materials

This study presents and assesses a new, user-friendly, and Excel-based Monte Carlo photon transport code, specifically designed to simulate Gaussian energy broadened response spectra of gamma-ray detector materials. This vectorized code utilizes the EPICS2017 photoatomic data library, with unionized energy grid construction, to facilitate precise and efficient photon transport simulations in any user-defined materials composition. Performance benchmarks were conducted using PHITS 3.31, with both basic and detailed models of a gamma-ray scintillator system, and detection crystals of NaI(Tl) and LaBr3(Ce). These benchmarks spanned a wide array of photon source monoenergetic emissions. Detector model efficiency was determined through automated peak area analysis, employing algorithms from the Ortec GammaVision software. The results indicated only minor spectral differences between the Excel-based code and the PHITS models, particularly within the range of practical gamma-ray energies and near-zero source-detector distances. Efficiency curves demonstrated good agreement between the Excel-based code and PHITS models, especially within the energy range of approximately 200 keV to a few MeV, with increasing agreement as detector crystal size increased. Comparisons were conducted with the existing data in literature at source-detector distances of 2 cm and 5 cm, using a detector crystal size of 2”. These comparisons support the code’s validity and its implementation of a basic geometry. This new Excel-based code appears as a promising tool with potential applications in radiation detector characterization and materials research.

Optical photon transport simulations for SiPM based PET scanner

Positrons emitted from radioisotope quickly annihilates to two collinear photons that are captured by the array of scintillator detectors to infer about the origin of radioactivity. Coincidence collection of pair of points from the detector modules makes a line of response, their large number reveals a radioactive region of interest. Most of the medical diagnostic research is conducted with position sensitive imaging device whose performance strongly depends on the optical light collection efficiency. A versatile Lutetium–Yttrium Oxyorthosilicate (LYSO) scintillator is used to mimic the optical photon production and transport processes followed by the light collection with Silicon PhotoMultiplier sensor. Charge spectrum from a 2 × 2 × 10 mm3 single LYSO bar is obtained in GEANT4, and reproduces well the scintillation characteristics of LYSO along with photons incidence angle distribution with a single SiPM pad. This excercise is followed by the simulation performance of two PET modules comprises of 12 × 12 SiPM tile coupled with mesh of LYSO bars. The preliminarly results shows the transverse position resolution (FWHM) as 7.82 mm for a point source. Objective of this study is to search for a cheaper detector solution which will have equivalent imaging performance.

Neu(t)ralMC: energy-efficient open source Monte Carlo algorithm for assessing photon transport in turbid media.

Light propagation in turbid mediums such as atmosphere, fluids, and biological tissues is a challenging problem which necessitates accurate simulation techniques to account for the effects of multiple scattering. The Monte Carlo method has long established itself as a gold standard and is widely adopted for simulating light transport, however, its computationally intensive nature often requires significant processing power and energy consumption. In this paper a novel, open source Monte Carlo algorithm is introduced which is specifically designed for use with energy-efficient processors, effectively addressing those challenges, while maintaining the accuracy/compatibility and outperforming existing solutions. The proposed implementation optimizes photon transport simulations by exploiting the unique capabilities of Apple's low-power, high-performance M-family of chips. The developed method has been implemented in an open-source software package, enabling seamless adaptation of developed algorithms for specific applications. The accuracy and performance are validated using comprehensive comparison with existing solvers commonly used for biomedical imaging. The results demonstrate that the new algorithm achieves comparable accuracy levels to those of existing techniques while significantly reducing computational time and energy consumption.

Graphical Visualisation of the MCNP Mesh Tally File

This paper presents an updated version of the MTV3D (Mesh Tally Visualization in 3D) program with Windows graphical user interface for visualization of the MCNP-based mesh tally files. Several improvements over the previous program version are addressing better figure export functionality ("Save as" option), switching between linear and logarithmic values on axes, dynamic figure scaling in active window, inversion of relative errors from “max” to “min” values, etc. MCNP is a well known and widely used general purpose Monte Carlo computer code for neutron, photon and electron transport simulation through arbitrary three-dimensional configurations. An important feature of the MCNP code is a graphical display of the simulation model using auxiliary program, such as X-window server, which is useful for geometry error-checking during model setup and visualisation of Monte Carlo results from a mesh tally file (i.e. meshtal file) over a structured xyz mesh. Such inspection of the model is useful for the end user, providing an insight of the Monte Carlo convergence process in a phase space and effectiveness of the selected variance reduction parameters in shielding calculations. Basic features and functionalities of the updated MTV3D program are presented on some selected hybrid-shielding problems involving ADVANTG3.0.3 and MCNP6.1.1b codes.

A generative adversarial network to speed up optical Monte Carlo simulations

Detailed simulation of optical photon transport and detection in radiation detectors is often used for crystal-based gamma detector optimization. However, the time and memory burden associated with the track-wise approach to particle transport and detection in commonly used Monte Carlo codes makes optical simulation prohibitive at a system level, where hundreds to thousands of scintillators must be modeled. Consequently, current large system simulations do not include detailed detector models to analyze the potential performance gain with new radiation detector technologies. Generative adversarial networks (GANs) are explored as a tool to speed up the optical simulation of crystal-based detectors. These networks learn training datasets made of high-dimensional data distributions. Once trained, the resulting model can produce distributions belonging to the training data probability distribution. In this work, we present the proof of concept of using a GAN to enable high-fidelity optical simulations of nuclear medicine systems, mitigating their computational complexity. The architecture of the first network version and high-fidelity training dataset is discussed. The latter is generated through accurate optical simulation with GATE/Geant4, and contains the position, direction, and energy distributions of the optical photons emitted by 511 keV gamma rays in bismuth germanate and detected on the photodetector face. We compare the GAN and simulation-generated distributions in terms of similarity using the Jensen–Shannon distance. Excellent agreement was found with similarity values higher than 93.5% for all distributions. Moreover, the GAN speeded the optical photon distribution generation by up to two orders of magnitude. These very promising results have the potential to drastically change the use of nuclear imaging system optical simulations by enabling high-fidelity system-level simulations in reasonable computation times. The ultimate is to integrate the GAN within GATE/Geant4 since numerous applications (large detectors, bright scintillators, Cerenkov-based timing positron emission tomography) can benefit from these improvements.

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality. To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. Speed-resolved perfusion was estimated in the three speed intervals 0 to , 1 to , and , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

Tutorial on Monte Carlo simulation of photon transport in biological tissues [Invited

A tutorial introduction to Monte Carlo (MC) simulation of light propagation in biological tissues. MC statistical sampling is introduced, the basic design of a MC program is explained, and examples of application in biomedicine are presented.

Monte Carlo Simulation of Photon Transport in Composite Materials

Monte Carlo Simulation of Photon Transport in Composite Materials

Performance estimation of optical skin probe in short wavelength infrared spectroscopy based on Monte-Carlo simulation

Optical throughput and optical path length are key parameters to obtain high signal to noise ratio and sensor sensitivity for the detection of skin tissue components based on short wavelength infrared (SWIR) spectroscopy. These parameters should be taken into account at the stage of optical system design. We aim to develop a method to estimate the optical efficiency and the effective water path length of a newly designed SWIR spectroscopy skin measurement system using Monte-Carlo photon migration simulation. To estimate the optical efficiency and the effective water path length, we investigated the characteristics of Monte-Carlo photon migration simulation utilizing one layered simple skin model. Simulation of photon transport in skin was conducted for transmission, transflection, and reflection optical configurations in both first overtone (1540 ~ 1820 nm) and combination (2040 ~ 2380 nm) wavelength ranges. Experimental measurement of skin spectrum was done using Fourier transform infrared spectroscopy based system to validate the estimation performance. Overall, the simulated results for optical efficiency and effective water path length are in good agreements with the experimental measurements, which shows the suggested method can be used as a means for the performance estimation and the design optimization of various in-vivo SWIR spectroscopic system.

Radiation shielding design of the CFETR polarimeter interferometer and CO2 dispersion interferometer

A three-wave based laser polarimeter/interferometer and a CO2 laser dispersion interferometer are used to determine the electron and current density profiles on a Chinese fusion engineering test reactor (CFETR). Radiation shielding is designed for the combination of polarimeter/interferometer and CO2 dispersion interferometer. Furthermore, neutronics models of the two systems are developed based on the engineering-integrated design of CFETR polarimeter/interferometer and CO2 dispersion interferometer and the major material components of CFETR. The polarimeter/interferometer and CO2 dispersion interferometer’s neutron and photon transport simulations were performed using the Monte Carlo neutral transport code to determine the energy deposition and neutron energy spectrum of the optical mirrors. The energy depositions of the first mirrors on the polarimeter/interferometer are reduced by three orders with the whole shielding. Since the mirrors of CO2 dispersion interferometer are very close to the diagnostic first wall, shielding space is limited and the CO2 dispersion interferometer energy deposition is higher than that of the polarimeter/interferometer. The dose rate after shutdown 106 s in the back-drawer structure has been estimated to be 83 μSv h−1 when the radiation shield is filled in the diagnostic shielding modules, which is below the design threshold of 100 μSv h−1. Radiation shielding design plays a key role in successfully applying polarimeter/interferometer and CO2 dispersive interferometer in CFETR.

Framework for denoising Monte Carlo photon transport simulations using deep learning.

.SignificanceThe Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens.AimWe aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method.ApproachWe developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms. We also developed a simple yet effective approach to creating synthetic datasets that can be used to train DL-based MC denoisers.ResultsOverall, DL-based image denoising algorithms exhibit significantly higher image quality improvements over traditional model-based denoising algorithms. Among the tested DL denoisers, our cascade network yields a 14 to 19 dB improvement in signal-to-noise ratio, which is equivalent to simulating to more photons. Other DL-based methods yielded similar results, with our method performing noticeably better with low-photon inputs and ResMCNet along with DRUNet performing better with high-photon inputs. Our cascade network achieved the highest quality when denoising complex domains, including brain and mouse atlases.ConclusionsIncorporating state-of-the-art DL denoising techniques can equivalently reduce the computation time of MC simulations by one to two orders of magnitude. Our open-source MC denoising codes and data can be freely accessed at http://mcx.space/.

The Impulse Approximation Scattering Function and Its Use in Monte Carlo Photon Transport Simulations

The viability of using the impulse approximation scattering function in Monte Carlo photon transport simulations is explored. This scattering function can be constructed from the double differential incoherent scattering cross section developed by Ribberfors and Berggren [Phys. Rev. A., Vol 26, p. 3325 (1982)]. A commonly used method for modeling photon Doppler broadening, which is referred to as the hybrid Doppler broadening method, can also be derived from this cross section. A new photon Doppler broadening method, called the consistent Doppler broadening method, is derived and discussed. This method eliminates some of the commonly employed approximations in the hybrid Doppler broadening method, in part, by using the impulse approximation scattering function. Integrated incoherent cross sections generated using the impulse approximation scattering function and the widely used Waller-Hartree scattering function are in good agreement above 20 keV. Below 20 keV, differences as high as 70% are observed, which differs from the roughly 5% differences observed by Ribberfors [Phys. Rev. A., Vol. 27, p. 3061 (1983)] for some of the materials. Integral and spectral quantities for two problems are also generated using the Monte Carlo photon transport capabilities of the Framework for Research in Nuclear Science and Engineering. Due to the small, relative result differences observed when using the impulse approximation scattering function, it is considered a viable alternative to the Waller-Hartree scattering function. In addition, some small, but expected, differences in spectral fluxes at low energies can be avoided by adopting the consistent Doppler broadening method.

Implementation and benchmarking of the local weight window generation function for OpenMC

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC's usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future.

Monte Carlo simulation of photon transport in a scattering-dominated medium with a refractive index gradient for acoustic light-guiding

Acoustic light waveguides use a technology that employs acoustic waves to create pressure-dependent refractive index distribution and guide light deep into biological tissue similar to an optical fiber. The method by which acoustic optical waveguides increase light transmission in biological tissues occurring inside scattering-dominated medium has not been clarified. To understand the phenomena occurring inside the scattering-dominated medium, we performed Monte Carlo simulations of photon transport in acoustic optical waveguides. The findings indicate that the larger the change in the refractive index in the scattering-dominated media, the greater the effect of photon confinement. In addition, as the refractive index gradient was increased, the near-field internal fluence was found to be greatly enhanced. The transition depth, which indicates the region where the internal fluence is enhanced by the refractive index gradient, was determined as a function of the radius at which the refractive index change is given.

Photon detection probability prediction using one-dimensional generative neural network

Photon detection is important for liquid argon detectors for direct dark matter searches or neutrino property measurements. Precise simulation of photon transport is widely used to understand the probability of photon detection in liquid argon detectors. Traditional photon transport simulation, which tracks every photon using the Geant4 simulation toolkit, is a major computational challenge for kilo-tonne-scale liquid argon detectors and GeV-level energy depositions. In this work, we propose a one-dimensional generative model which efficiently generates features using an -layer. This model bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail as the Geant4 simulation. The application to simulating photon detection systems in kilo-tonne-scale liquid argon detectors demonstrates this novel generative model is able to reproduce Geant4 simulation with good accuracy and 20 to 50 times faster. This generative model can be used to quickly predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.

Multi-scattering software part II: experimental validation for the light intensity distribution

This article, Part II of an article series on GPU-accelerated Monte Carlo simulation of photon transport through turbid media, focuses on the validation of the online software Multi-Scattering. While Part I detailed the implementation of the computational model, simulated and experimental results are now compared for the distribution of the scattered light intensity. The scattering phantoms prepared here are aqueous dispersions of polystyrene microspheres of diameter D = 0.5, 2 and 5 μm and at various concentrations, resulting in optical depth ranging from OD = 1 to 17.5. The Lorenz-Mie scattering phase functions used in the simulations have been verified experimentally at low particle concentrations by analyzing the angular light intensity distribution at the Fourier plane of a collecting lens. The validation approach herein accounts for the specific light collection and image formation by the camera. The front and side surfaces of the medium are imaged and the corresponding light intensity distributions are compared qualitatively and quantitatively. It is concluded that the model enables reliable simulations over the tested parameters, offering predictive simulations of transmitted intensities with a mean relative error ≤~19% over the full range. The online software is available at: https://multi-scattering.com/.