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Report is a Technical Standard report for FEMA NIRT project managed by NA-42 and describes XSim architecture and XML format as a means to minimize the effort required for a field application in producing data.
We designed and built an AI/ML model to detect anomalies in the DUNE far detector data. The model has been trained on simulated radiological background (rbkg) data, which is the major background for supernova burst neutrinos. The trained model was evaluated on both new samples of radiological backgrounds and supernova burst neutrino events in the elastic scattering and charged current interaction channels. We found that the trained model can successfully identify supernova burst neutrino events as anomalies while identifying radiological backgrounds as nominal events.
We designed and built an AI/ML model to detect anomalies in the DUNE far detector data. The model has been trained on simulated radiological background (rbkg) data, which is the major background for supernova burst neutrinos that we want to detect as anomaly in thios work. The trained model was evaluated on both new samples of radiological backgrounds and supernova burst neutrino events in the elastic scattering and charged current interaction channels. We found that the trained model can successfully identify supernova burst neutrino events as anomalies while identifying radiological backgrounds as nominal events.
The CEA has developed many compact characterization tools to follow sensitive operations in a nuclear environment. Usually, these devices are made to carry out radiological inventories, to prepare nuclear interventions or to supervise some special operations. These in situ measurement techniques mainly take place at different stages of clean-up operations and decommissioning projects, but they are also in use to supervise sensitive operations when the nuclear plant is still operating. In addition to this, such tools are often associated with robots to access very highly radioactive areas, and thus can be used in accident situations. Last but not least, the radiological data collected can be entered in 3D calculation codes used to simulate the doses absorbed by workers in real time during operations in a nuclear environment. Faced with these ever-greater needs, nuclear measurement instrumentation always has to involve on-going improvement processes. Firstly, this paper will describe the latest developments and results obtained in both gamma and alpha imaging techniques. The gamma camera has been used by the CEA since the 1990's and several changes have made this device more sensitive, more compact and more competitive for nuclear plant operations. It is used to quickly identify hot spots, locating irradiating sources from 50 keV to 1500 keV. Several examples from a wide field of applications will be presented, together with the very latest developments. The alpha camera is a new camera used to see invisible alpha contamination on several kinds of surfaces. The latest results obtained allow real time supervision of a glove box cleaning operation (for {sup 241}Am contamination). The detection principle as well as the main trials and results obtained will be presented. Secondly, this paper will focus on in situ gamma spectrometry methods developed by the CEA with compact gamma spectrometry probes (CdZnTe, LaBr{sub 3}, NaI, etc.). The radiological data collected is used to quantify the activity of hot spots and can also then be entered in 3D models of nuclear plants to simulate intervention scenarios. Recent developments and results will be presented regarding this. Finally, thanks to a large amount of feedback, the interest of using complementary measurements will be discussed. In fact, the recent use of 3D simulation codes requires very accurate knowledge of nuclear plant radiological data. The use of coupled devices such as imaging devices, (gamma and alpha cameras), gamma spectrometry, dose rate mapping, collimated / un-collimated measurements and many other physical values gives an approach to the radiological knowledge of a process or plant with the lowest possible uncertainty. In line with this, the paper will conclude with the future developments and trials that could be assessed in that field of application. (authors)
The results of the National Atmospheric Release Advisory Center (NARAC) model simulations are compared to measured data from the Full-Scale Radiological Dispersal Device (FSRDD) field trials. The series of explosive radiological dispersal device (RDD) experiments was conducted in 2012 by Defence Research and Development Canada (DRDC) and collaborating organizations. During the trials, a wealth of data was collected, including a variety of deposition and air concentration measurements. The experiments were conducted with one of the stated goals being to provide measurements to atmospheric dispersion modelers. These measurements can be used to facilitate important model validation studies. For this study, meteorological observations recorded during the tests are input to the diagnostic meteorological model, ADAPT, which provides 3–D, time-varying mean wind and turbulence fields to the LODI dispersion model. LODI concentration and deposition results are compared to the measured data, and the sensitivity of the model results to changes in input conditions (such as the particle activity size distribution of the source) and model physics (such as the rise of the buoyant cloud of explosive products) is explored. The NARAC simulations predicted the experimentally measured deposition results reasonably well considering the complexity of the release. Lastly, changes to the activity size distribution of the modeled particles can improve the agreement of the model results to measurement.
Purpose: Portal dosimetry provides an accurate and convenient means of verifying dose delivered to the patient. A simple method for carrying out portal dosimetry for volumetric modulated arc therapy (VMAT) is described, together with phantom measurements demonstrating the validity of the approach. Methods: Portal images were predicted by projecting dose in the isocentric plane through to the portal image plane, with exponential attenuation and convolution with a double-Gaussian scatter function. Appropriate parameters for the projection were selected by fitting the calculation model to portal images measured on an iViewGT portal imager (Elekta AB, Stockholm, Sweden) for a variety of phantom thicknesses and field sizes. This model was then used to predict the portal image resulting from each control point of a VMAT arc. Finally, all these control point images were summed to predict the overall integrated portal image for the whole arc. The calculated and measured integrated portal images were compared for three lung and three esophagus plans delivered to a thorax phantom, and three prostate plans delivered to a homogeneous phantom, using a gamma index for 3% and 3 mm. A 0.6 cm{sup 3} ionization chamber was used to verify the planned isocentric dose. The sensitivity of this method to errors in monitor units, field shaping, gantry angle, and phantom position was also evaluated by means of computer simulations. Results: The calculation model for portal dose prediction was able to accurately compute the portal images due to simple square fields delivered to solid water phantoms. The integrated images of VMAT treatments delivered to phantoms were also correctly predicted by the method. The proportion of the images with a gamma index of less than unity was 93.7% ± 3.0% (1SD) and the difference between isocenter dose calculated by the planning system and measured by the ionization chamber was 0.8% ± 1.0%. The method was highly sensitive to errors in monitor units and field shape, but less sensitive to errors in gantry angle or phantom position. Conclusions: This method of predicting integrated portal images provides a convenient means of verifying dose delivered using VMAT, with minimal image acquisition and data processing requirements.
Purpose: To provide a method for calculating the transmission of any broad photon beam with a known energy spectrum in the range of 20–1090 keV, through concrete and lead, based on the superposition of corresponding monoenergetic data obtained from Monte Carlo simulation. Methods: MCNP5 was used to calculate broad photon beam transmission data through varying thickness of lead and concrete, for monoenergetic point sources of energy in the range pertinent to brachytherapy (20–1090 keV, in 10 keV intervals). The three parameter empirical model introduced byArcher et al. [“Diagnostic x-ray shielding design based on an empirical model of photon attenuation,” Health Phys. 44, 507–517 (1983)] was used to describe the transmission curve for each of the 216 energy-material combinations. These three parameters, and hence the transmission curve, for any polyenergetic spectrum can then be obtained by superposition along the lines of Kharrati et al. [“Monte Carlo simulation of x-ray buildup factors of lead and its applications in shielding of diagnostic x-ray facilities,” Med. Phys. 34, 1398–1404 (2007)]. A simple program, incorporating a graphical user interface, was developed to facilitate the superposition of monoenergetic data, the graphical and tabular display of broad photon beam transmission curves, and the calculation of material thickness required for a given transmission from these curves. Results: Polyenergetic broad photon beam transmission curves of this work, calculated from the superposition of monoenergetic data, are compared to corresponding results in the literature. A good agreement is observed with results in the literature obtained from Monte Carlo simulations for the photon spectra emitted from bare point sources of various radionuclides. Differences are observed with corresponding results in the literature for x-ray spectra at various tube potentials, mainly due to the different broad beam conditions or x-ray spectra assumed. Conclusions: The data of this work allow for the accurate calculation of structural shielding thickness, taking into account the spectral variation with shield thickness, and broad beam conditions, in a realistic geometry. The simplicity of calculations also obviates the need for the use of crude transmission data estimates such as the half and tenth value layer indices. Although this study was primarily designed for brachytherapy, results might also be useful for radiology and nuclear medicine facility design, provided broad beam conditions apply.
Any industrial plant whatever the industry is will face its end of life. In the nuclear field decommissioning is inevitable for returning to green. Because of the specificity of each plant, decommissioning projects are never the same. The choice of a specific strategy for each operation during a scenario has cascade consequences on the generated waste, extension of due date, costs and risks. Decommissioning in nuclear industry is a new domain where countries have a limited experience. When starting a decommissioning project engineers have to gather some data needed for planning and realizing the best scenario strategy. Scenario which should consider the costs, waste management, received dose and the planning. The quality of the input data has a strong influence on the risks and should be analyzed with care to reduce all doubts for the study. Nuclear plants running decommissioning programs concerns old plants with poorly documented history resulting in missing or uncertain data. The ambient dose rate is not always well-known as dose measurement or samples have not been taken or updated for many years. This kind of data may vary quite a lot when radiological events happen. An efficient way to reduce contingencies is to go through simulations. It allows determining the best scenario strategy for each set of data while evaluating the influence of all uncertainties. (authors)
Leveraging the depth and breadth of solutions generated through crowdsourcing can be a powerful accelerator to method development for high consequence problems. While data science competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the general underlying problem of interest to the host. This paper outlines important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition. These include: precisely defining the scope of the problem, encouraging participation by competitors from diverse technical backgrounds, specifying the most interesting solution space in order to encourage improvement and distinguish between competitors, strategically generating data sets that enable testing for interpolation and extrapolation to new scenarios of interest, leveraging design of experiment principles for strategic data design while preventing unintentional artifacts in the competition data set that an algorithm could exploit without addressing the real problem of interest, and carefully designing the leaderboard scoring metric to select top solutions that exactly match the overall competition goals. The methods are illustrated with a recently completed competition in the context of urban radiological search to evaluate algorithms capable of detecting, identifying, and locating radioactive materials. Simulated data were used in the urban search competition. Ideas for using measured real data in competitions are also suggested
The deposited energy and dose distribution of beams of protons and carbon over a head are simulated using the free tool package Geant4 and the data analysis package ROOT-C++. The present work shows a methodology to understand the microscopical process occurring in a session of hadron-therapy using advance simulation tools.
Purpose: The availability of accurate and simple models for the estimation of x-ray spectra is of great importance for system simulation, optimization, or inclusion of photon energy information into data processing. There is a variety of publicly available tools for estimation of x-ray spectra in radiology and mammography. However, most of these models cannot be used directly for modeling microfocus x-ray sources due to differences in inherent filtration, energy range and/or anode material. For this reason the authors propose in this work a new model for the simulation of microfocus spectra based on existing models for mammography and radiology, modified to compensate for the effects of inherent filtration and energy range. Methods: The authors used the radiology and mammography versions of an existing empirical model [tungsten anode spectral model interpolating polynomials (TASMIP)] as the basis of the microfocus model. First, the authors estimated the inherent filtration included in the radiology model by comparing the shape of the spectra with spectra from the mammography model. Afterwards, the authors built a unified spectra dataset by combining both models and, finally, they estimated the parameters of the new version of TASMIP for microfocus sources by calibrating against experimental exposure data from a microfocus x-ray source. The model was validated by comparing estimated and experimental exposure and attenuation data for different attenuating materials and x-ray beam peak energy values, using two different x-ray tubes. Results: Inherent filtration for the radiology spectra from TASMIP was found to be equivalent to 1.68 mm Al, as compared to spectra obtained from the mammography model. To match the experimentally measured exposure data the combined dataset required to apply a negative filtration of about 0.21 mm Al and an anode roughness of 0.003 mm W. The validation of the model against real acquired data showed errors in exposure and attenuation in line with those reported for other models for radiology or mammography. Conclusions: A new version of the TASMIP model for the estimation of x-ray spectra in microfocus x-ray sources has been developed and validated experimentally. Similarly to other versions of TASMIP, the estimation of spectra is very simple, involving only the evaluation of polynomial expressions.
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s). Conclusions: Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT.
Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.
Purpose: Current 4D-MRI techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of an entirely new framework of 4D-MRI based on k-space retrospective sorting. Methods: An important challenge of the proposed technique is to determine the number of repeated scans(NR) required to obtain sufficient k-space data for 4D-MRI. To do that, simulations using 29 cancer patients' respiratory profiles were performed to derive the relationship between data acquisition completeness(Cp) and NR, also relationship between NR(Cp=95%) and the following factors: total slice(NS), respiratory phase bin length(Lb), frame rate(fr), resolution(R) and image acquisition starting-phase(P0). To evaluate our technique, a computer simulation study on a 4D digital human phantom (XCAT) were conducted with regular breathing (fr=0.5Hz; R=256×256). A 2D echo planer imaging(EPI) MRI sequence were assumed to acquire raw k-space data, with respiratory signal and acquisition time for each k-space data line recorded simultaneously. K-space data was re-sorted based on respiratory phases. To evaluate 4D-MRI image quality, tumor trajectories were measured and compared with the input signal. Mean relative amplitude difference(D) and cross-correlation coefficient(CC) are calculated. Finally, phase-sharing sliding window technique was applied to investigate the feasibility of generating ultra-fast 4D-MRI. Result: Cp increased with NR(Cp=100*[1-exp(-0.19*NR)], when NS=30, Lb=100%/6). NR(Cp=95%) was inversely-proportional to Lb (r=0.97), but independent of other factors. 4D-MRI on XCAT demonstrated highly accurate motion information (D=0.67%, CC=0.996) with much less artifacts than those on image-based sorting 4D-MRI. Ultra-fast 4D-MRI with an apparent temporal resolution of 10 frames/second was reconstructed using the phase-sharing sliding window technique. Conclusions: A novel 4D-MRI technology based on k-space sorting has been successfully developed and evaluated on the digital phantom. Framework established can be applied to a variety of MR sequences, showing great promises to develop the optimal 4D-MRI technique for many radiation therapy applications. NIH (1R21CA165384-01A1)
Fast and accurate predictions of the flow and transport of materials in urban and complex terrain areas are challenging because of the heterogeneity of buildings and land features of different shapes and sizes connected by canyons and channels, which results in complex patterns of turbulence that can enhance material concentrations in certain regions. To address this challenge, we have developed an efficient three-dimensional computational fluid dynamics (CFD) code called Aeolus that is based on first principles for predicting transport and dispersion of materials in complex terrain and urban areas. The model can be run in a very efficient Reynolds average Navier–Stokes (RANS) mode or a detailed large eddy simulation (LES) mode. The RANS version of Aeolus was previously validated against field data for tracer gas and radiological dispersal releases. As a part of this work, we have validated the Aeolus model in LES mode against two different sets of data: (1) turbulence quantities measured in complex terrain at Askervein Hill; and (2) wind and tracer data from the Joint Urban 2003 field campaign for urban topography. As a third set-up, we have applied Aeolus to simulate cloud rise dynamics for buoyant plumes from high-temperature explosions. For all three cases, Aeolus LES predictions compare well to observations and other models. These results indicate that Aeolus LES can be used to accurately simulate turbulent flow and transport for a wide range of applications and scales.
Purpose: Differential phase contrast CT (DPC-CT) is emerging as a new technology to improve the contrast sensitivity of conventional attenuation-based CT. The noise equivalent quanta as a function over spatial frequency, i.e., the spectrum of noise equivalent quanta NEQ(k), is a decisive indicator of the signal and noise transfer properties of an imaging system. In this work, we derive the functional form of NEQ(k) in DPC-CT. Via system modeling, analysis, and computer simulation, we evaluate and verify the derived NEQ(k) and compare it with that of the conventional attenuation-based CT. Methods: The DPC-CT is implemented with x-ray tube and gratings. The x-ray propagation and data acquisition are modeled and simulated through Fresnel and Fourier analysis. A monochromatic x-ray source (30 keV) is assumed to exclude any system imperfection and interference caused by scatter and beam hardening, while a 360 Degree-Sign full scan is carried out in data acquisition to avoid any weighting scheme that may disrupt noise randomness. Adequate upsampling is implemented to simulate the x-ray beam's propagation through the gratings G{sub 1} and G{sub 2} with periods 8 and 4 {mu}m, respectively, while the intergrating distance is 193.6 mm (1/16 of the Talbot distance). The dimensions of the detector cell for data acquisition are 32 Multiplication-Sign 32, 64 Multiplication-Sign 64, 96 Multiplication-Sign 96, and 128 Multiplication-Sign 128 {mu}m{sup 2}, respectively, corresponding to a 40.96 Multiplication-Sign 40.96 mm{sup 2} field of view in data acquisition. An air phantom is employed to obtain the noise power spectrum NPS(k), spectrum of noise equivalent quanta NEQ(k), and detective quantum efficiency DQE(k). A cylindrical water phantom at 5.1 mm diameter and complex refraction coefficient n= 1 -{delta}+i{beta}= 1 -2.5604 Multiplication-Sign 10{sup -7}+i1.2353 Multiplication-Sign 10{sup -10} is placed in air to measure the edge transfer function, line spread function and then modulation transfer function MTF(k), of both DPC-CT and the conventional attenuation-based CT. The x-ray flux is set at 5 Multiplication-Sign 10{sup 6} photon/cm{sup 2} per projection and observes the Poisson distribution, which is consistent with that of a micro-CT for preclinical applications. Approximately 360 regions, each at 128 Multiplication-Sign 128 matrix, are used to calculate the NPS(k) via 2D Fourier transform, in which adequate zero padding is carried out to avoid aliasing in noise. Results: The preliminary data show that the DPC-CT possesses a signal transfer property [MTF(k)] comparable to that of the conventional attenuation-based CT. Meanwhile, though there exists a radical difference in their noise power spectrum NPS(k) (trait 1/|k| in DPC-CT but |k| in the conventional attenuation-based CT) the NEQ(k) and DQE(k) of DPC-CT and the conventional attenuation-based CT are in principle identical. Conclusions: Under the framework of ideal observer study, the joint signal and noise transfer property NEQ(k) and detective quantum efficiency DQE(k) of DPC-CT are essentially the same as those of the conventional attenuation-based CT. The findings reported in this paper may provide insightful guidelines on the research, development, and performance optimization of DPC-CT for extensive preclinical and clinical applications in the future.
Purpose: To assess the performance of two approaches to the system response matrix (SRM) calculation in pinhole single photon emission computed tomography (SPECT) reconstruction. Methods: Evaluation was performed using experimental data from a low magnification pinhole SPECT system that consisted of a rotating flat detector with a monolithic scintillator crystal. The SRM was computed following two approaches, which were based on Monte Carlo simulations (MC-SRM) and analytical techniques in combination with an experimental characterization (AE-SRM). The spatial response of the system, obtained by using the two approaches, was compared with experimental data. The effect of the MC-SRM and AE-SRM approaches on the reconstructed image was assessed in terms of image contrast, signal-to-noise ratio, image quality, and spatial resolution. To this end, acquisitions were carried out using a hot cylinder phantom (consisting of five fillable rods with diameters of 5, 4, 3, 2, and 1 mm and a uniform cylindrical chamber) and a custom-made Derenzo phantom, with center-to-center distances between adjacent rods of 1.5, 2.0, and 3.0 mm. Results: Good agreement was found for the spatial response of the system between measured data and results derived from MC-SRM and AE-SRM. Only minor differences for point sources at distances smaller than the radius of rotation and large incidence angles were found. Assessment of the effect on the reconstructed image showed a similar contrast for both approaches, with values higher than 0.9 for rod diameters greater than 1 mm and higher than 0.8 for rod diameter of 1 mm. The comparison in terms of image quality showed that all rods in the different sections of a custom-made Derenzo phantom could be distinguished. The spatial resolution (FWHM) was 0.7 mm at iteration 100 using both approaches. The SNR was lower for reconstructed images using MC-SRM than for those reconstructed using AE-SRM, indicating that AE-SRM deals better with the projection noise than MC-SRM. Conclusions: The authors' findings show that both approaches provide good solutions to the problem of calculating the SRM in pinhole SPECT reconstruction. The AE-SRM was faster to create and handle the projection noise better than MC-SRM. Nevertheless, the AE-SRM required a tedious experimental characterization of the intrinsic detector response. Creation of the MC-SRM required longer computation time and handled the projection noise worse than the AE-SRM. Nevertheless, the MC-SRM inherently incorporates extensive modeling of the system and therefore experimental characterization was not required.
The International Atomic Energy Agency (IAEA) has initiated an effort to provide stakeholder support services as part of Phase II of the Constraints to Implementing Decommissioning and Environmental Remediation (CIDER II) programs. The intent of this sub-group of CIDER II is to provide members states support in areas of stakeholder communication, engagement and participation, with the goal of building trust in decommissioning and environmental remediation (D and ER) decisions for radioactive contamination and facilities. IAEA has also initiated a follow-on effort to address Modeling and Data for Radiological Impact Assessments (MODARIA II), which is aimed at enhancing the capabilities of Member States to simulate radionuclide transfer in the environment, and to ensure appropriate levels of protection from ionizing radiation. Working Group 1 of MODARIA II is focused on the integration of modeling and decision analysis. These two groups have complementary and supporting objectives, which provides an opportunity for collaboration and mutual benefit. The technical integrator is the approach of structured decision making, which follows a five-step process that combines stakeholder values with models through a technically defensible decision analysis. Briefly, the steps are: 1. Provide the context and decision context for the problem; 2. Obtain costs, values and concerns from stakeholders, to develop objectives that need to be achieved (e.g., improve human health, minimize costs, increase jobs); 3. Identify options that might achieve the objectives (e.g., cleanup, institutional controls, education); 4. Develop probabilistic models that connect options to objectives so that the objectives can be evaluated collectively (e.g., a fate and transport and risk model to address the objective of reducing human health risk, a cost model to address costs of taking action); and, 5. Model evaluation, including sensitivity analysis, to determine if an action can be taken with sufficient certainty, or if collection of more data is needed to decrease uncertainty before a decision is made. This approach also supports adaptive management as new data are collected. This approach clarifies the roles of stakeholders and subject matter experts in the decision-making process, where stakeholders specify values, and subject matter experts focus on the models that are needed to evaluate the options. It also provides a technically defensible, reproducible, transparent and traceable approach to all of the components necessary to properly solve the types of complex decision problems that plague radioactive waste management, decommissioning and environmental remediation. This paper introduces the major challenges to implementing such a broad scale of decision analysis, and approaches to overcoming those challenges, which include not only the technical challenges, but also the need to develop capacity in a range of skills not usually accommodated in environmental modeling and includes the need for the IAEA to address decision making in different countries, and with different cultures, religions, political affiliations, and languages. This paper explores the potential for IAEA to benefit from both the CIDER II and MODARIA II programs, and hence to develop a fully integrated approach to effective decision making for radioactive waste management and environmental remediation. Note that there are many people involved in these efforts at the IAEA, and credit belongs to those people for the progress that has been made to date. Our intent here is simply to introduce these IAEA programs at the WM2018 Symposium to provide our perspective and some awareness of some of the directions in which IAEA appears to be headed regarding stakeholder engagement, environmental and risk modeling, data science, and decision analysis. (authors)