Full Monte Carlo Simulation Research Articles

Constraints on the extragalactic magnetic field strength from blazar spectra based on 145 months of Fermi-LAT observations

ABSTRACT Properties of the extragalactic magnetic field (EGMF) outside of clusters and filaments of the large-scale structure are essentially unknown. The EGMF could be probed with γ-ray observations of distant (redshift z > 0.1) blazars. TeV γ-rays from these sources are strongly absorbed on extragalactic background light photons; secondary electrons and positrons produce cascade γ-rays with the observable flux dependent on EGMF parameters. We put constraints on the EGMF strength using 145 months of Fermi-LAT observations of the blazars 1ES 1218+304, 1ES 1101−232, and 1ES 0347−121, and imaging atmospheric Cherenkov telescope observations of the same sources. We perform a series of full direct Monte Carlo simulations of intergalactic electromagnetic cascades with the elmag 3.01 code and construct a model of the observable spectra inside the point spread functions of the observing instruments for a range of EGMF strengths. We compare the observed spectra with the models for various values of the EGMF strength B and calculate the exclusion statistical significance for every value of B. We find that the values of the EGMF strength B ≤ 10−17 G are excluded at a high level of the statistical significance Z > 4σ for all the four options of the intrinsic spectral shape considered (power law, power law with exponential cutoff, log-parabola, and log-parabola with exponential cutoff). The value of B = 10−16 G is not excluded; it is still a viable option of the EGMF strength. These results were obtained for the case of steady sources.

Evaluation of five different sediment fingerprinting approaches for estimating sediment source contributions in an arid region

Fingerprinting methods are widely used to quantify sediment provenance at a watershed scale. However, different fingerprinting methods often yield different estimates for the same watershed. The objectives are to discuss in detail the performance of five fingerprinting approaches, compare the efficiency of various solutions, and investigate the effects of the number of composite fingerprints and the number of tracers in a composite fingerprint on the estimation accuracy. Source samples were collected from three typical geomorphic areas of the Dune, Gobi, and Mountains in the Danghe Reservoir watershed in Northwest China, and sediment samples from the Danghe reservoir. Overall results suggested that a multiple composite fingerprints approach was superior to a single or few composite fingerprints approach. Generally, increasing the number of composite fingerprints tended to improve the estimation accuracy. The preferred methods of solving the mixing model should depend upon the number of composite fingerprints. When the number of composite fingerprints is large, analytical solutions, compared to numerical solutions, are preferred as the computation is simple while providing an exact solution and better estimation by eliminating fitting errors. For a single composite fingerprint or only a few composite fingerprints, Monte Carlo (MC) simulation, which can statistically increase sample number, is favored to obtain a reliable estimation. However, for a moderate number of composite fingerprints, direct optimization using average tracer concentrations without MC simulation may be desirable, because it tends to yield similar accuracy to a full MC simulation while avoiding intensive computation. There was no clear relationship between the number of tracers in a composite fingerprint and the estimation accuracy, as the optimal number of tracers is situation dependent. Results also indicate that a composite fingerprint with higher discriminant ability may not necessarily translate to better estimation, as it is also affected by tracers’ conservatism and measurement errors.

Specific Absorbed Fractions for Spontaneous Fission Neutron Emitters in the ICRP Reference Pediatric Voxel Phantom Series.

Specific absorbed fractions (SAFs) are key components in the workflow of internal exposure assessment following the intake of a radionuclide, allowing quick conversion of particle energy released in a source region to the expected absorbed dose in target regions throughout the body. For data completeness, SAFs for spontaneous fission neutron emitters are currently needed for the recently adopted ICRP reference pediatric voxel phantom series. With 77 source regions within each reference individual and 28 radionuclides decaying via spontaneous fission, full Monte Carlo simulation requires significant computation time. In order to reduce this burden, a novel method for neutron SAF estimation was undertaken. The Monte Carlo N-Particle version 6.1 (MCNP6) simulation package was chosen to simulate the 252 Cf Watt fission neutron spectrum originating from 15 source regions in each phantom; dose estimation within 41 target tissues allowed for assessment of the SAF value for each source-target pair. For the remaining source regions, chord length distributions were computed using MATLAB code to determine the separation between the source-target pairs within the pediatric phantom series. These distance distributions were used in conjunction with a 252 Cf neutron dose point kernel calculated in soft tissue, which was modified to account for the source region's depth from the surface of the body. Lastly, the 252 Cf SAF dataset was extended to the other 27 spontaneous fission neutron emitters based on differences in the Watt fission spectrum parameters of each radionuclide. This methodology has been shown to accurately estimate spontaneous fission neutron SAFs to within 20% of the Monte Carlo estimated value for most source-target pairs in the ICRP reference pediatric series.

Accuracy of two dosimetry software programs for 177Lu radiopharmaceutical therapy using voxel-based patient-specific phantoms

PurposeVirtual dosimetry using voxel-based patient-specific phantoms and Monte Carlo (MC) simulations offer the advantage of having a gold standard against which absorbed doses may be benchmarked to establish the dosimetry accuracy. Furthermore, these reference values assist in investigating the accuracy of the absorbed dose methodologies from different software programs. Therefore, this study aimed to compare the accuracy of the absorbed doses computed using LundADose and OLINDA/EXM 1.0. MethodsThe accuracy was based on 177Lu-DOTATATE distributions of three voxel-based phantoms. SPECT projection images were simulated for 1, 24, 96, and 168 h post-administration and reconstructed with LundADose using 3D OS-EM reconstruction. Mono-exponential curves were fitted to the bio-kinetic data for the kidneys, liver, spleen, and tumours resulting in SPECT time-integrated activity (SPECT-TIA). The SPECT-TIA were used to compute mean absorbed doses using LundADose (LND-DSPECT) and OLINDA (OLINDA-DSPECT) for the organs. Pre-defined true activity images, were used to obtain TRUE-TIA and, together with full MC simulations, computed the true doses (MC-DTrue). The dosimetry accuracy was assessed by comparing LND-DSPECT and OLINDA-DSPECT to MC-DTrue. ResultsOverall, the results presented an overestimation of the mean absorbed dose by LND-DSPECT compared to the MC-DTrue with a dosimetry accuracy ≤6.6%. This was attributed to spill-out activity from the reconstructed LND-DSPECT, resulting in a higher dose contribution than the MC-DTrue. There was a general underestimation (<8.1%) of OLINDA-DSPECT compared to MC-DTrue attributed to the geometrical difference in shape between the voxel-based phantoms and the OLINDA models. Furthermore, OLINDA-DSPECT considers self-doses while MC-DTrue reflects self-doses plus cross-doses. ConclusionThe better than 10% accuracy suggests that the mean dose values obtained with LND-DSPECT and OLINDA-DSPECT approximate the true values. The mean absorbed doses of the two software programs, and the gold standard were comparable. This work shall be of use for optimising 177Lu dosimetry for clinical applications.

Study of phonon transport across Si/Ge interfaces using Full-Band phonon Monte Carlo simulation

A Full Band Monte Carlo simulator has been developed to consider phonon transmission across interfaces disposed perpendicularly to the heat flux. This solver of the Boltzmann transport equation does not require any assumption on the shape the phonon distribution and can naturally consider all phonon transport regimes from the diffusive to the fully ballistic regime. This simulator is used to study single and double Si/Ge heterostructures from the micrometer scale down to the nanometer scale, i.e. in all phonon transport regime from fully diffusive to ballistic. A methodology to determine the thermal conductivity at thermal interfaces is presented. It is also shown that the different transport regimes are correlated to different spectral contributions of the phonon modes to the heat flux along the devices. This local indicator of the transport regime gives new insights into the out-of-equilibrium phonon transport near the interfaces.

Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D‐UNet generative adversarial networks

Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. The energy depositions used for the training of the GAN are obtained using full Geant4 MC simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140 × 18 × 18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The generator network has a U-Net structure and is designed to predict the energy depositions of the photon beam inside three phantoms of variable geometry with increasing complexity. The critic network is a relatively simple convolutional network, which is trained to distinguish energy depositions predicted by the generator from the ones obtained with the full MC simulation. The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. Inside the most realistic phantom, a simple pediatric head, the model predictions deviate by less than 1% of the maximal energy deposition from the simulations in more than 96% of the in-field voxels. For all three phantoms, the model generalizes the energy deposition predictions well to phantom geometries, which have not been used for training the model but are interpolations of the training data in multiple dimensions. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model. The proposed GAN model predicts dose distributions inside unknown phantoms with only small deviations from the full MC simulation with computations times of less than a second. It demonstrates good interpolation ability to unseen but similar phantom geometries and is flexible enough to be trained on data with different radiation scenarios without the need for optimization of the model parameter. This proof-of-concept encourages to apply and further develop the model for the use in MRT treatment planning, which requires fast and accurate predictions with sub-mm resolutions.

Toward three-dimensional patient-specific internal dosimetry using GATE Monte Carlo technique

Patient-specific internal dosimetry with a high accuracy is one of the most significant issues in the field of nuclear medicine. In recent investigations, there has been a dramatic alter in the different methods for computing the correct organ doses from the injected radioactivity. Monte Carlo simulations have yielded highly accurate results for voxel-based dosimetry techniques. This paper aims to present a precise methodology based on full Monte Carlo simulations, and time-activity curve estimation using three individual scans over 90 min. In this study, a dataset of patients who were injected with F-Fluor-2-D-Glucose, F18-FDG (10 normal cases, 60 ± 8.2 kg, injected activity, 370 ± 10 MBq), was acquired in 3 different time sequences (20, 60, 90 min post-injection). First, the GATE Monte Carlo results were validated using experimental measurements. Subsequently, the Monte Carlo code was considered for S-value calculations. The CT and PET images were used to obtain attenuation maps and activity distribution inside the patients' phantom, respectively. The CT and PET images were registered using an image processing software to achieve the same matrix and pixel sizes. Regions of interest, such as the kidneys, spleen, bladder, lung, pancreas, liver, stomach, and heart wall, were identified on CT images. The segmented ROIs of the CT images were reconstructed and the voxelized phantom and voxelized source of each patient at specific time intervals were generated. The time activity curves (TACs) were obtained based on PET images. The results of the F18-FDG dose distribution in patient-specific phantoms indicate that the bladder, lung, heart, and liver absorbed more activity than other organs, as expected from the PET images. Close agreement was observed between the results obtained in this study and those of previous investigations that used biokinetic models. The dose per activity values was reported within 0.02–0.06 mGy/MBq for most organs with simulation uncertainty less than 7%. There is ample room for further progress in the clinical use of the Monte Carlo method and the improvement of the phantom-based model.

Fast Monte Carlo-Based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Inverse planning is an essential tool for optimizing the delivered radiation dose on low-dose-rate (LDR) prostate brachytherapy. Clinical inverse planning systems use the TG-43 dose computation formalism in order to perform a fast optimization. However, this method is an approximation that often leads to a dose overestimation, resulting on suboptimal plans. Alternatively, Monte Carlo simulation (MCS) can be used to obtain an accurate dose distribution, but considerably increasing the estimation time. We propose a fast inverse planning method for LDR prostate brachytherapy that uses a deep convolutional neural network (DCNN) trained on a graphics processing unit (GPU)-based MCS generated database to estimate the dose distribution on the prostate and organs at risk. Segmentations of the organs and seeds&#x2019; positions are given as the DCNN input. The mean percent error on the test set was &#x2212;1.19&#x00B1;0.94&#x0025; within the prostate. The DCNN was used to estimate the dosimetric parameters in each organ for every configuration of the optimization loop. The dosimetric parameters of the final DCNN-based brachytherapy plans were in good agreement compared to the same plans recalculated with a full MCS. The proposed inverse planning based on DCNN was capable to reach an equivalent level of accuracy with Monte Carlo with a runtime in less than 1 min using conventional GPU card.

Quantitatively predicting modal thermal conductivity of nanocrystalline Si by full-band Monte Carlo simulations

Thermal transport of nanocrystalline Si is of great importance for the application of thermoelectrics. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected to benefit the efficiency of thermoelectrics. In this work, the variance reduced Monte Carlo simulation with full band of phonon dispersion is applied to study the modal thermal conductivity of nanocrystalline Si. Importantly, the phonon modal transmissions across the grain boundaries which are modeled by the amorphous Si interface are calculated by the mode-resolved atomistic Greens function method. The predicted ratios of thermal conductivity of nanocrystalline Si to that of bulk Si agree well with that of the experimental measurements in a wide range of grain size. The thermal conductivity of nanocrystalline Si is decreased from 54 percent to 3 percent and the contribution of phonons with mean free path larger than the grain size increases from 30 percent to 96 percnet as the grain size decreases from 550 nm to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Greens function method can capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing high performance Si based thermoelectrics.

Prospects for single-photon sideband cooling of optically trapped neutral atoms

We propose a novel cooling scheme for realising single photon sideband cooling on particles trapped in a state-dependent optical potential. We develop a master rate equation from an ab-initio model and find that in experimentally feasible conditions it is possible to drastically reduce the average occupation number of the vibrational levels by applying a frequency sweep on the cooling laser that sequentially cools all the motional states. Notably, this cooling scheme works also when a particle experiences a deeper trap in its internal ground state than in its excited state, a condition for which conventional single photon sideband cooling does not work. In our analysis, we consider two cases: a two-level particle confined in an optical tweezer and Li atoms confined in an optical lattice, and find conditions for efficient cooling in both cases. The results from the model are confirmed by a full quantum Monte Carlo simulation of the system Hamiltonian. Our findings provide an alternative cooling scheme that can be applied in principle to any particle, e.g. atoms, molecules or ions, confined in a state-dependent optical potential.

Prospects for chargino pair production at the CEPC * *Supported by the National Key Programme (2018YFA0404000)

The proposed Circular Electron Positron Collider (CEPC) with a center-of-mass energy GeV will primarily serve as a Higgs factory. Additionally, it will offer good opportunities to search for new physics phenomena at low energies, which can be challenging with hadron colliders; however, these discoveries are highly motivated by theoretical models developed to explain, e.g., the relic abundance of dark matter. This paper presents sensitivity studies for chargino pair production by considering scenarios for both a Bino-like and a Higgsino-like neutralino as the lightest supersymmetric particle and using a full Monte Carlo (MC) simulation. By assuming systematic uncertainties at the level of 5%, the CEPC has the ability to discover chargino pair production up to the kinematic limit of for both scenarios. The results have a minor dependence on the reconstruction model and detector geometry. These results can also be considered as a reference and benchmark for similar searches at other proposed electron-positron colliders, such as the Future Circular Collider ee (FCC-ee) or the International Linear Collider (ILC), given the similar nature of the facilities, detectors, center-of-mass energies, and target luminosities.

A perspective on the theoretical and numerical aspects of Ion Mobility Spectrometry

Ion Mobility Spectrometry (IMS) has become a ubiquitous analytical technique, in particular when used as an orthogonal technique to Mass Spectrometry (MS). As separations of ions in the gas phase become more precise, the need to provide a suitable theory that explains the observed differences is apparent. While the theory exists, much of it is obscured due to the difficulty of the equations and the approximations to the solution. This work explores some of the more useful theoretical approaches to IMS while making use of a full Monte Carlo simulations algorithm to provide some pedagogical examples that characterize the reasons behind the different theoretical approaches, and whether they need to be used for a particular calculation. To improve the existing theory, reliable empirical data is required. For such reason, an appropriate labeling system for mobility is proposed here requiring that at least the temperature, gas, electric field, and instrument employed are provided and which is an extension of the previous protocol.

Intrinsic ferromagnetism in CoBr2 nanolayers: a DFT + U and Monte Carlo study

The intrinsic ferromagnetism of CoBr2 bulk was investigated using DFT (density functional theory) combined with the full potential linear augmented plane wave method and Monte Carlo simulations. The ground state of CoBr2 exhibits ferromagnetic behavior and a semiconductor character. We used the generalized gradient approximation (GGA) and GGA + U (Hubbard correction) approximations to determinate the magnetic moment. The magnetic moment reached the experimental value and was in good agreement with the other theoretical values. The value obtained was used as an input to a Monte Carlo study to calculate the thermal magnetization and magnetic hysteresis cycles. Ferromagnetic behavior was observed and was found to be due to an positive exchange interaction. These results lead us to believe that this material could be a promising spintronic material.

Technical note: GAMMORA, a free, open-source, and validated GATE-based model for Monte-Carlo simulations of the Varian TrueBeam.

Monte Carlo (MC) is the reference computation method for medical physics. In radiotherapy, MC computations are necessary for some issues (such as assessing figures of merit, double checks, and dose conversions). A tool based on GATE is proposed to easily create full MC simulations of the Varian TrueBeam STx. GAMMORA is a package that contains photon phase spaces as a pre-trained generative adversarial network (GAN) and the TrueBeam's full geometry. It allows users to easily create MC simulations for simple or complex radiotherapy plans such as VMAT. To validate the model, the characteristics of generated photons are first compared to those provided by Varian (IAEA format). Simulated data are also compared to measurements in water and heterogeneous media. Simulations of 8 SBRT plans are compared to measurements (in a phantom). Two examples of applications (a second check and interplay effect assessment) are presented. The simulated photons generated by the GAN have the same characteristics (energy, position, and direction) as the IAEA data. Computed dose distributions of simple cases (in water) and complex plans delivered in a phantom are compared to measurements, and the Gamma index (3%/3mm) was always superior to 98%. The feasibility of both clinical applications is shown. This model is now shared as a free and open-source tool that generates radiotherapy MC simulations. It has been validated and used for five years. Several applications can be envisaged for research and clinical purposes.

Study on the lower absolute measurement limit of air kerma from 137Cs source using a large-size cavity chamber

For the purpose of obtaining the lower absolute measurement limit of the air kerma in 137Cs γ-ray reference fields, a cavity chamber with a volume of 301.936(19) cm3 was designed and fabricated. The absolute measurement of the air kerma was performed at the minimum effective dose rate of the large-size cavity chamber in our 137Cs γ-ray reference field. With the help of the detailed Monte-Carlo (MC) simulations of the large-size cavity chamber and our 137Cs irradiation facility, the conversion coefficient from the cavity dose to the reference air kerma was calculated. And the obtained air kerma rate with the MC simulation method was 1.429(13) mGy/h. At the same condition, the measured air kerma rates with the cavity theory method and the substitute method were 1.428(13) mGy/h and 1.419(53) mGy/h, respectively. The measurement result with the full MC simulation method agrees with the other two measurement results within 0.07% and 0.70%, respectively. The combination of the large-size chamber and the full MC simulation method can help calibration laboratories achieve the absolute measurement of the low air kerma rate (such as 1.5 mGy/h) in the 137Cs γ-ray reference field.

Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels.

Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with 90Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Materials and Methods: Two tissue-specific DVKs for 90Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations. A method was developed to produce a 3D dose map by convolving the calculated DVKs with the activity biodistribution derived from clinical single-photon emission computed tomography (SPECT) or positron emission tomography (PET) images. Image registration for the SPECT or PET images with the corresponding computed tomography scans was performed before dosimetry calculation. The authors first compared the DVK convolution dosimetry with a direct full MC simulation on an XCAT anthropomorphic phantom. They then tested it in 25 individual clinical cases of patients who underwent 90Y therapy. All MC simulations were carried out using the GATE MC toolkit. Results: Comparison of the measured absorbed dose using tissue-specific DVKs and direct MC simulation on 25 patients revealed a mean difference of 1.07% ± 1.43% for the liver and 1.03% ± 1.21% for the tumor tissue, respectively. The largest difference between DVK convolution and full MC dosimetry was observed for the lung tissue (10.16% ± 1.20%). The DVK statistical uncertainty was <0.75% for both media. Conclusions: This semiautomatic algorithm is capable of performing rapid, accurate, and efficient 3D dosimetry. The proposed method considers tissue and activity heterogeneity using tissue-specific DVKs. Furthermore, this method provides results in <1 min, making it suitable for everyday clinical practice.

A commercial treatment planning system with a hybrid dose calculation algorithm for synchrotron radiotherapy trials

Synchrotron Radiotherapy (SyncRT) is a preclinical radiation treatment which delivers synchrotron x-rays to cancer targets. SyncRT allows for novel treatments such as Microbeam Radiotherapy, which has been shown to have exceptional healthy tissue sparing capabilities while maintaining good tumour control. Veterinary trials in SyncRT are anticipated to take place in the near future at the Australian Synchrotron's Imaging and Medical Beamline (IMBL). However, before veterinary trials can commence, a computerised treatment planning system (TPS) is required, which can quickly and accurately calculate the synchrotron x-ray dose through patient CT images. Furthermore, SyncRT TPS's must be familiar and intuitive to radiotherapy planners in order to alleviate necessary training and reduce user error. We have paired an accurate and fast Monte Carlo (MC) based SyncRT dose calculation algorithm with EclipseTM, the most widely implemented commercial TPS in the clinic. Using EclipseTM, we have performed preliminary SyncRT trials on dog cadavers at the IMBL, and verified calculated doses against dosimetric measurement to within 5% for heterogeneous tissue-equivalent phantoms. We have also performed a validation of the TPS against a full MC simulation for constructed heterogeneous phantoms in EclipseTM, and showed good agreement for a range of water-like tissues to within 5%–8%. Our custom EclipseTM TPS for SyncRT is ready to perform live veterinary trials at the IMBL.

Monte Carlo calculation of charge collection efficiencies in ionization chambers

Collection efficiency is an important quantity in dosimetry with ionization chambers. It can be calculated by solving a hyperbolic system of partial differential equations. This system can be solved only in few, simple, idealized geometries, but for more realistic designs an analytical resolution is no longer possible. In the present work a Monte Carlo scheme that could permit to calculate the collection efficiency for any ionization chamber geometry is proposed. This scheme has been tested against Boag’s approach for three chambers with plane-parallel, cylindrical and spherical geometries, operated in the recombination regime. The results obtained in the full Monte Carlo simulation closely agree with the Boag’s ones for the three ideal geometries considered. The largest relative difference, ∼0.3%, has been found for the plane-parallel chamber in case of 50 V, the lowest potential difference investigated in this study. Results appear to be stable against changes in the chamber volume, the ion mobility and the recombination constant. The method proposed could be a useful tool to calculate collection efficiencies of ionization chambers, provided the electric field inside them is known.

A semi-empirical model for scatter field reduction in digital mammography

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%–10% range for a 35–70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids.

Simulation code for estimating external gamma-ray doses from a radioactive plume and contaminated ground using a local-scale atmospheric dispersion model.

In this study, we developed a simulation code powered by lattice dose-response functions (hereinafter SIBYL), which helps in the quick and accurate estimation of external gamma-ray doses emitted from a radioactive plume and contaminated ground. SIBYL couples with atmospheric dispersion models and calculates gamma-ray dose distributions inside a target area based on a map of activity concentrations using pre-evaluated dose-response functions. Moreover, SIBYL considers radiation shielding due to obstructions such as buildings. To examine the reliability of SIBYL, we investigated five typical cases for steady-state and unsteady-state plume dispersions by coupling the Gaussian plume model and the local-scale high-resolution atmospheric dispersion model using large eddy simulation. The results of this coupled model were compared with those of full Monte Carlo simulations using the particle and heavy-ion transport code system (PHITS). The dose-distribution maps calculated using SIBYL differed by up to 10% from those calculated using PHITS in most target locations. The exceptions were locations far from the radioactive contamination and those behind the intricate structures of building arrays. In addition, SIBYL's computation time using 96 parallel processing elements was several tens of minutes even for the most computationally expensive tasks of this study. The computation using SIBYL was approximately 100 times faster than the same calculation using PHITS under the same computation conditions. From the results of the case studies, we concluded that SIBYL can estimate a ground-level dose-distribution map within one hour with accuracy that is comparable to that of the full Monte Carlo simulation.