Shaheen Dewji

Releases from nuclear or radiological security events can result in significant internal radiation contamination through inhalation of particulate contaminants. The International Commission on Radiological Protection (ICRP) has developed the reference Human Respiratory Tract Model (HRTM), detailed in ICRP Publications 66 and updated in the ICRP Publication 130, to estimate the deposition and clearance of inhaled radionuclides. Biokinetic models further estimate retention and excretion of internalized particulates, aiding the derivation of inhalation dose coefficients (DC). The HRTM developed by the ICRP utilizes deterministic quantities outlined in the ICRP Publication 66 and 130. The overarching goal of this study was to determine the variability from deterministic biokinetic/dosimetry models to represent the stochastic breadth of radionuclide metabolism in an exposed occupational population from realistic source terms, yielding an expanded compendium of inhalation dose coefficients. The analysis was carried out in three phases: (1) Development of an enhanced biokinetic and dose coefficient model and computational module based on ICRP Publication 130 HRTM and associated element specific systemic biokinetics; (2) Investigation of uncertain parameters in the HRTM; and (3) Stochastic analysis using Latin Hypercube Sampling, incorporating non-parametric (Kolmogorov-Smirnov statistics) test and Q-Q plots, informing parametric method, to characterize the distribution of the committed effective dose coefficients. To determine the most impactful parameters among the uncertain parameters, a Random Forest regression model was employed for feature importance, coupled with SHapley Additive exPlanations (SHAP) for comprehensive machine learning interpretation of the features. This study presents a unique stochastic framework for modeling inhaled particulate metabolism, enhancing capabilities in radiation consequence management, medical countermeasure development, and radiation dose reconstruction for epidemiological investigations.

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

This paper develops a hazard- and pathway-based framework for high-level radioactive waste (HLW) disposal grounded in intrinsic radionuclide decay characteristics, geochemical behavior, and comparative hazard. We examine the physical and geochemical properties of key radionuclides and quantify lifetime cancer risk from chronic ingestion on a per-unit-mass basis using established regulatory models. Long-lived radionuclides are weakly radioactive and emit little or no penetrating gamma radiation; their hazards are therefore dominated by internal exposure pathways, analogous to those of chemical carcinogens commonly disposed of in the shallow subsurface. Actinides exhibit cancer risks comparable to dioxin but are strongly immobilized under reducing deep-geological conditions, while mobile long-lived radionuclides are associated with lower carcinogenic risk than typical persistent chemical contaminants. These findings support a paradigm shift in disposal strategies: (a) from heavily engineered containment systems toward nature-based approaches for ensuring long-term post-closure safety that explicitly leverage intrinsic radionuclide properties, along with slow release from waste forms and diffusion-limited transport, assuming appropriate site selection and geological stability; and (b) toward consideration of lifecycle perspectives and trade-offs between future hypothetical risks and present-day actual environmental impacts, including material use and fuel-cycle emissions. We further highlight asymmetries between radioactive and chemical waste stewardship. Public institutions and regulatory authorities are already responsible for actively managing large inventories of persistent chemical carcinogens in the shallow subsurface indefinitely. Increased efforts are needed to integrate radiological and chemical hazards within a unified environmental risk framework to establish more coherent, lifecycle-aware waste management strategies across industries.

Executive Order (EO) 14300, issued 23 May 2025, calls for a comprehensive reform of the Nuclear Regulatory Commission (NRC) to improve transparency, regulatory efficiency, and scientific coherence in nuclear oversight. While the EO focuses on NRC oversight authority, its stated aims include improving interagency coordination, which creates a timely opportunity to modernize and harmonize radiation protection standards across all federal agencies. Currently, federal agencies including the NRC, Department of Energy (DOE), Environmental Protection Agency (EPA), Occupational Safety and Health Administration (OSHA), Department of Transportation (DOT), and Department of Defense (DOD) use incongruous regulatory frameworks that differ in their radiation dose limits, units, dosimetric models, and compliance methodologies, resulting in implementation inefficiencies and inconsistencies in both risk communication and regulatory enforcement. This paper outlines key recommendations to streamline US radiation protection policy while maintaining robust public and worker safety, improving scientific transparency, and aligning with international best practices. KEY RECOMMENDATIONS Harmonize Radiation Dose Limits Across Federal Agencies There should be a unified set of radiation dose limits and protection criteria applied across all federal agencies that have jurisdiction over any aspect of radiation protection, including but not limited to the NRC, DOE, EPA, OSHA, DOT, and DOD. These limits should: Use consistent dosimetry models, i.e., the most recent International Commission on Radiological Protection (ICRP) recommendations given in Publication 103 (see also #6). Eliminate duplicative or inconsistent agency-specific requirements, particularly EPA’s groundwater, air, and site decommissioning limits, that are not aligned with NRC’s dose-based approach under 10 CFR 20. These legacy risk-based standards create regulatory conflicts, delay cleanups, and undermine public confidence in consistent federal protection criteria. Eliminate over-conservative assumptions in derivation of effluent limits. Depoliticize Low-Dose Risk Debates — Focus on Practical Protection Scientific discourse regarding health effects at low doses, along with continued debate over the application of the linear no-threshold (LNT) model as a regulatory construct, should not paralyze regulatory policy. Instead, radiation protection policy at all levels – federal, state, and local – should: Focus on the principles of justification and optimization, as described in ICRP Publication 103. Justification ensures that the net benefit is achievable, while optimization ensures that the net benefit is maximized. Both must consider all factors (e.g. safety, economic viability, environmental impacts, etc.), not just radiological ones. Apply ALARA only to contexts where it provides demonstrable protective value, such as occupational exposure management, medical exposures, and emergency response scenarios. In such cases, what is considered “reasonable” may vary; for example, diagnostic imaging requirements may warrant higher exposures. Clarify that ALARA is not required or expected for public exposures already below regulatory limits. This is particularly relevant in environmental cleanup or in scenarios where background radiation dominates total exposure. Establish a De Minimis Threshold for Regulation Define a scientifically justified, risk-informed “below regulatory concern” or de minimis dose threshold, under which no regulatory action is required. This threshold would prioritize regulatory attention toward exposures with meaningful radiological impact while avoiding unnecessary control of exposures that are well below background variability. A practical benchmark should be recommended by the National Council on Radiation Protection and Measurements (NCRP) as an authority chartered by the U.S. Congress to provide radiation protection expertise, alongside stakeholders and regulators, in a transparent, scientifically defensible process. The authors posit that a practical de minimis dose might be on the order of 0.2 mSv y-1 (20 mrem y-1)1 above background, representing approximately 20–30% of the combined cosmic and terrestrial components of natural background. This value could serve as a candidate threshold for consensus-based scientific evaluation, consistent with international approaches to exclusion and exemption. This approach would further support proportionality in regulation, improve resource allocation, and enhance clarity in public communication. Maintain Current Dose Limits While Enhancing Practicality Maintain the current federal dose limits in 10 CFR 20.1201 and 20.1301 and 10 CFR 35.75 of: 1 mSv y-1 (100 mrem y-1) for the public. 50 mSv y-1 (5 rem y-1) for occupational exposure (and maintain other occupational dose limits in 20.1201 for lens of eye, skin dose, etc.). 5 mSv (0.5 rem) for any individual from exposure to a released patient. These limits are scientifically supported, internationally aligned, and operationally achievable. Rather than pursuing ALARA as minimization to zero dose, an interpretation not grounded in regulatory intent, agencies should emphasize best practices to justify and optimize exposure levels (see also #3). Justification ensures the net benefit of the practice outweighs the radiological detriment, while optimization considers technical feasibility, economic cost, and protection objectives. Revise NRC and other agency guidance (e.g., NUREG-1530 Rev.1) to reflect these practical approaches and to provide a unified, interagency framework for radiation protection. A single, harmonized guidance document would improve consistency, reduce misaligned standards among federal agencies, and support coherent implementation of radiation protection policy. Conduct Periodic Scientific Updates Without Disrupting Policy Establish a structured mechanism for both reviewing and incorporating updated scientific data, such as dose coefficients, biokinetic and dosimetric models, and epidemiological findings, based on consensus-driven scientific recommendations from bodies such as the ICRP and NCRP, into the regulations on a periodic basis (e.g., every 5 y). These updates should be implemented through interagency coordination with NCRP, ICRP, and relevant technical bodies, while maintaining stability in overarching dose limits. This approach allows regulatory frameworks to reflect evolving science without introducing uncertainty or disruption in compliance obligations. Ensure an Independent Regulatory Authority To maintain nuclear safety and public confidence, the NRC must retain its operational independence from undue political or commercial influence. While subject to appropriate Congressional oversight, the agency’s regulatory decisions should remain grounded in technical expertise and statutory authority. Preserving this independence is especially important in the context of Executive Order 14300, which emphasizes interagency coordination but must not be interpreted as a mandate for political intervention in technical decision-making. Standardize the Use of SI Units All federal agencies should adopt and uniformly apply the International System of Units (SI) in radiation protection standards and public communications. Despite long-standing recognition of SI units as the international standard, federal regulatory language continues to reference non-SI units such as rem and curie, creating inconsistency and increasing the likelihood of misinterpretation. Transitioning to SI units exclusively would align US practices with ICRP and IAEA recommendations, reduce conversion-related errors, and simplify training and compliance across agencies, licensees, and the public. This transition should include revising existing regulatory language, updating compliance guidance, and phasing out dual-unit reporting. CONCLUSION EO 14300 presents a time-sensitive opportunity to unify, modernize, and harmonize the US system of radiation protection. By advancing consistent standards, updating technical foundations, and clarifying regulatory expectations across federal agencies, the recommendations outlined here promote scientifically grounded and operationally practical reform. These actions support the protection of public and worker health while improving clarity, reducing unnecessary duplication, and enhancing public trust in federal oversight. Acknowledgements The authors acknowledge the contributions to these recommendations from the hundreds of attendees at the open forums sponsored by the Health Physics Society and National Council on Radiation Protection and Measurements. The attendees represented a broad cross-section of the radiation protection community and provided input that informed the recommendations presented here.

Simulating particle deposition in the respiratory tract requires high computational effort due to the intricate airway geometry and complex airflow–particle interactions. To address this challenge, this study introduces the first demonstration of Dynamic Mode Decomposition (DMD) as a reduced-order model to infer the trajectories of inhaled particles during a breathing cycle and to evaluate the applicability of DMD as a fluid field interpolator. The periodic nature of respiration and the predominance of sinusoidal boundary conditions make it well-suited for DMD analysis. Three high-fidelity computational fluid dynamics (CFD) simulations were performed under three different inlet volume airflow conditions for the same realistic adult male anthropomorphic phantom respiratory tract model. Reduced-rank DMD reconstructions were compared to the CFD ground truth, yielding a Mean Relative Error (MRE) of 12% in the velocity field. Additionally, a fourth simulation was conducted at an intermediate point to evaluate the interpolation capability of the parametric DMD framework in complex systems. This interpolation resulted in an MRE of 20%, with the reconstructed flow field capturing dominant fluid modes and overall dynamics, though localized discrepancies reached relative errors up to 70%. While DMD effectively reconstructed fluid fields, preserving mean flow regimes, some deviations were observed in Lagrangian particle tracking, specifically in spatial deposition resolution. However, the method approximated overall particle distribution with an 85% correlation to ground truth and was effective in representing regional deposition patterns across the tracheobronchial tree. These findings support the utility of DMD a computationally efficient approach for fluid field reconstruction and particle transport analysis in respiratory flow simulations. • Dynamic Mode Decomposition proves effective for extrapolating flow features and preserving large-scale fluid structures. • Dynamic Mode Decomposition ability to interpolate intermediate parameters is limited in high complexity geometries. • Dynamic Mode Decomposition successfully captured the overall particle trajectories but accumulated positional errors in regions of complex flow; however, the approach proved effective for predicting statistical deposition patterns.

Abstract Dose estimation in response to internal radionuclide exposures requires reconstruction of the initial intake activity, which is frequently unknown due to the absence of a priori data. In such scenarios, intake is inferred from bioassay measurements obtained at one or more time points post-exposure. Reconstructing an initial intake from bioassay relies on biokinetic models that describe the body distribution and clearance of the toxicant. These models typically employ first-order differential equations with generalised population parameters, which do not capture individual variation in metabolism or anatomy. Thus, reconstruction of initial intakes is affected by multiple sources of stochasticity, including physical deposition of the inhaled radionuclide, detection system uncertainty, and inter-individual physiological variability. The capacity of machine learning (ML) algorithms to model highly non-linear and often stochastic processes makes them appropriate for augmenting intake reconstruction. This study applies artificial neural networks to estimate the initial intake activity of 90 Sr inhaled by beagles. Model performance and sensitivity to input data quality were assessed through inclusion of individual-specific features, such as age, weight, and sex. Three data regimens were systematically designed, each with distinct pre-processing pipelines and model complexity. The first regimen demonstrates feasibility of intake reconstruction using bioassay measurements taken within 14 days post-exposure. The second regimen demonstrates that summary statistics of retention functions in historical records lack sufficient resolution for individualised ML modelling. The third regimen shows that historical dose estimates, despite limitations in resolution and methodology, can be used as surrogate features when multiple post-exposure time points are available. Root mean squared error was used to evaluate prediction error, while a custom metric, the variance relative difference, quantified model bias. In addition to evaluating predictive performance, this study assesses the integrity and usability of historical data from 90 Sr beagle inhalation experiments conducted at the Inhalation Toxicology Research Institute between 1966 and 1987.

External exposure due to secondary photons (predominantly bremsstrahlung) generated from electron source emissions in environmental soil are of concern due to their ability to deposit significant amounts of ionizing energy to organs and tissues within the body. The "condensed history method" employed in many modern Monte Carlo (MC) codes may be used to simulate secondary photon yields (given as photons per beta decay) arising from electron source emissions with relatively few assumptions regarding the secondary photon spatial, energy, and angular dependencies. These yields may in turn be used to derive protection quantities such as secondary photon effective dose rate (DR) and risk coefficients for a variety of idealized external exposure scenarios. Use of the condensed history method is, however, computationally burdensome when simulating idealized external exposure scenarios even with available parallel computing resources. Consequently, use of the method was largely prohibitive for prior environmental dosimetry and risk assessment applications that required innumerable MC simulations for deriving secondary photon protection quantities. A MC method has herein been proposed for estimating secondary photon yields from electron source emissions in environmental soil with the condensed history method in a computationally feasible manner using the Monte Carlo N-Particle version 6.2 (MCNP6.2) radiation transport code. The proposed method was demonstrated with radiation transport models of idealized external exposure scenarios patterned after Federal Guidance Report (FGR) 15, and secondary photon yields determined using the proposed method and a previously adopted analytical method were compared.

INTRODUCTION: Inhalation is a primary pathway for the intake of airborne particles, including radioactive materials, accounting for up to 73% of annual exposure to natural sources of radiation. Among workers, inhalation is the most common route of radionuclide intake, according to the European Commission's radiation protection technical report. The significance of inhalation exposure increases in military operational environments and occupations involving airborne radioactive particles, such as nuclear weapons testing, accident cleanups, and handling of radioactive materials. The likelihood of inhalation exposure and respiratory risks varies depending on environmental releases, occupational settings, and deployment-related activities. The deposition of inhaled aerosols depends on a range of factors broadly classified as environmental conditions and individual characteristics. The complex interactions among these factors necessitate theoretical models, supported by empirical data, to accurately predict deposition patterns. This study developed a customizable computational tool for predicting particle deposition in the human respiratory tract, optimizing accuracy, efficiency, and practicality. These attributes are particularly suited to military exposure scenarios, where timely and precise assessments are critical for evaluating exposure levels and implementing protective measures. The tool contributes to a broader initiative to safeguard lung health in active duty service members, veterans, military beneficiaries, and the general public exposed to radionuclide or toxic metal contamination from radiological or nuclear events. MATERIALS AND METHODS: To predict aerosol deposition in the human respiratory tract, the algorithm of the International Commission on Radiological Protection (ICRP) Publication 66 human respiratory tract model was adopted and integrated into an in-house Python based computational tool, REDCALdep. This tool incorporates updates from ICRP Publication 130 and allows for enhanced customization, including physiological parameters tailored to military exposure guidelines (MEGs), individualized deposition computations, and sensitivity analyses. RESULTS: The deposition fractions (DFs) calculated using REDCALdep for a reference worker were benchmarked against ICRP-published values, demonstrating excellent agreement. Relative differences in total DFs were below 5% for both aerodynamic and thermodynamic lognormal particle size distribution, with most differences under 1%. Region specific differences were generally less than 3%, largely attributable to numerical precision and differences in rounding schemes. Additionally, REDCALdep uses a quantile range for particle sizes in the lognormal distribution, influencing the precision of the computed DF. Sex-weighted DFs were computed based on a 14-day MEG-defined activity schedule, encompassing sleeping, sedentary, light, and heavy activities. Compared to ICRP reference worker deposition values, the magnitude of the maximum relative differences in regional DFs were approximately 72%, with total deposition fractions differing by up to 49.44%. CONCLUSION: These findings emphasize the importance of the newly reported MEG-specific deposition data. The MEG-informed DFs reported in this study represent a novel dataset that enhances internal dose assessments and risk evaluations specific to military scenarios. Through its customizable and precise deposition calculations, REDCALdep serves as a valuable tool for decision-making related to the inhalation of toxic and radioactive materials in occupational and military context, as well as for individualized dose assessments.

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations - including turbulence and particle-wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

Abstract In radiological and nuclear emergencies, military personnel and first responders are at elevated risk of internal contamination via inhalation of airborne radionuclides. Rapid in-vivo assessments are required for efficient triage, regulatory compliance, and medical intervention. This study investigates the impact of chest wall thickness (CWT) on lung counting efficiency using military-specific mesh-type human computational phantoms that represent the current standards and anthropomorphic parameters of U.S. members of the military. A 2″ × 2″ NaI(Tl) scintillation detector with digital base was modeled and benchmarked against experimental measurements using polymethyl methacrylate slab phantoms to assess attenuation effects. Monte Carlo simulations in Particle and Heavy Ion Transport code System were employed to characterize lung deposition of radionuclides, with variations in CWT examined across different anthropometric models. Results demonstrated an inverse exponential relationship between CWT and detector peak counting efficiency, with minor deviations in female phantoms due to geometric constraints. These results support improved calibration approaches for in-vivo radiation detection systems and enable more consistent internal contamination assessments across a range of body types during emergency response operations.

Estimates of organ-absorbed and committed doses to individuals exposed to radioactive materials via acute inhalation often rely on internal dose coefficients and detector responses from reference human computational models. To achieve more accurate dose assessments to United States Armed Forces service members exposed in-field, computational models with varying morphometric parameters representative of this population are necessary. The International Commission on Radiological Protection (ICRP) Publication 145 provides detailed mesh reference computational phantoms (MRCPs) for adult males and females, with morphometric parameters matched to the 50th percentile. Previously, these phantoms were 2D and 3D scaled to match desired height, mass, and secondary anthropomorphic parameters in the creation of the University of Florida / Memorial Sloan Kettering (UF/MSK) computational phantom library. To achieve body fat percentage targets required for accession into the US Armed Forces, muscle and fat volumes were adjusted accordingly, thus, creating the UF/Department of Defence computational phantom library presented in this study. A comprehensive library of mesh-type computational human phantoms was created, including 57 adult males and 49 adult females with morphometric parameters aligned with United States Armed Forces service members. Phantoms were restricted to a body mass index between 19 and 27.5, with body fat percentages below 26% for males and 36% for females. Specific absorbed fractions were computed for selected source and target combinations, demonstrating how variations in height and body mass influence energy absorption in target regions relative to the ICRP MRCPs. Radiation detector responses were also computed, revealing that higher body masses resulted in decreased registered counts in the detection volume. These findings highlight the importance of incorporating morphometric variability in computational phantoms to achieve more accurate dose assessments and radiation detection responses for United States Armed Forces service members who inhale radioactive materials in-field.

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

In response to radiological or nuclear emergencies, prompt assessment of internal radiation contamination is critical for triage assessment and medical countermeasures administrations. The NAIS-2’2 NaI(Tl) scintillation detectors were selected to construct a scanning system to measure internalized gamma-ray emitting radionuclide contamination in warfighters. A mesh-type adult female anthropomorphic phantom of 165 cm height and 65 kg weight was used, and an eight-point detector grid was implemented covering critical body areas (head, neck, chest, abdomen, and hip) implemented in the Monte-Carlo simulation tool, PHITS. The gamma depositions from critical radionuclides were simulated across all organs of interests. Biokinetic modeling data of each radionuclide were incorporated to determine the time-dependent retention in these organs and the gamma depositions in the detectors. To optimize the number and position of detectors for contamination characterization, a scoring algorithm was developed by analyzing the pseudo planes across the anterior and posterior of the phantom. These planes were subdivided into fine meshes to simulate the gamma flux, which were then integrated with biokinetics to compute the detector counting efficiencies at various times post-inhalation. This integration will enable the identification of the optimal detector array configuration, achieving the best counting statistics for various combinations of radionuclide sources, times post-exposure, and phantom morphologies, and the reconstruction of total contamination patterns.

This study introduces a comprehensive and automated framework for personalized particle deposition assessment in the human respiratory tract (HRT), overcoming the limitations of traditional models and offering a method for subject-specific studies. We introduce a novel computational framework, generating individualized particle deposition profiles and detailed dose maps leveraging 3D reconstructed HRT from Computed Tomography (CT) scans and advanced Computational Fluid and Particle Dynamics (CFPD) simulations. The work integrates 3D geometry reconstruction from CT scans using computer vision algorithms, geometry preprocessing for CFPD simulation readiness, pre-converged CFPD parameters for modeling airflow and particle simulation, and Monte Carlo source card generation for internal dose assessment from inhaled radioactive particles. Validation and verification of CFPD simulations confirmed convergence using a mesh with a base size of 1 mm. The workflow was tested on 14 different HRT geometries under various breathing conditions. The impaction parameter was used in our analysis to compare particle deposition efficiency results to those of previous literature, confirming our workflow accuracy. Particle and Heavy Ion Transport code System (PHITS) source cards were automatically generated using activity-weighted point sources from the CFPD-informed particle distribution profile. Our results showed a difference as high as 90 % in the absorbed fraction by specific organs compared to PHITS simulations using uniform particle distribution in the HRT. The presented automated workflow allows researchers to reduce the human hours spent on this task, typically spanning several hours or days, to a matter of minutes. This approach addresses the anatomical and physiological variations inherent in HRT, which are crucial for drug delivery systems, targeted respiratory therapies, and assessing inhaled radioactive particles at a subject-specific level.

Abstract The International Commission on Radiological Protection (ICRP) has developed the reference Human Respiratory Tract Model (HRTM), detailed in ICRP Publications 66 and 130, to estimate the deposition and clearance of inhaled radionuclides. These models utilize reference anatomical and physiological parameters for particle deposition (PD). Biokinetic models further estimate retention and excretion of internalized particulates, aiding the derivation of inhalation dose coefficients (DC). This study aimed to assess variability in deterministic 131 I biokinetic and dosimetry models through stochastic analysis using the updated HRTM from ICRP Publication 130. The complexities of the ICRP PD model were reconstructed into a new, independent computational model. Comparison with reference data for total PD fractions for reference worker, solely a nose breather, covering activity median aerodynamic diameters from 0.3 μ m to 20 μ m, showed a 1.04% relative and 0.7% absolute difference, demonstrating good agreement with ICRP deposition fractions. The deterministic DC module was reconstructed in Python and expanded for stochastic analysis, systematically expanding deposition components from HRTM and assigning probability distribution functions to uncertain parameters. These were integrated into an in-house stochastic radiological exposure dose calculator, utilizing latin hypercube sampling. A case of an occupational radionuclide intake was explored, in which biodistribution and committed effective DC (CEDC) were computed for 131 I type F, considering a lognormal particle size distribution with a median of 5 μ m. Results showed the published ICRP reference CEDC marginally exceeds the 75th percentile of observed samples, with log-gamma distribution as the best-fit probability distribution. A Random Forest regression model with SHapley Additive exPlanations was employed for sensitivity analysis to predict feature importance. The analysis identified the HRTM particle transport rates scaling factor, followed by the aerodynamic deposition efficiency in the alveolar interstitial region as the most impactful parameters. This study offers a unique stochastic approach on inhaled particulate metabolism, enhancing radiation consequence management, medical countermeasures, and dose reconstruction for epidemiological studies.

Abstract Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence. Graphical abstract

Biokinetic models have been employed in internal dosimetry (ID) to model the human body's time-dependent retention and excretion of radionuclides. Consequently, biokinetic models have become instrumental in modelling the body burden from biological processes from internalized radionuclides for prospective and retrospective dose assessment. Solutions to biokinetic equations have been modelled as a system of coupled ordinary differential equations (ODEs) representing the time-dependent distribution of materials deposited within the body. In parallel, several mathematical algorithms were developed for solving general kinetic problems, upon which biokinetic solution tools were constructed. This paper provides a comprehensive review of mathematical solving methods adopted by some known internal dose computer codes for modelling the distribution and dosimetry for internal emitters, highlighting the mathematical frameworks, capabilities, and limitations. Further discussion details the mathematical underpinnings of biokinetic solutions in a unique approach paralleling advancements in ID. The capabilities of available mathematical solvers in computational systems were also emphasized. A survey of ODE forms, methods, and solvers was conducted to highlight capabilities for advancing the utilization of modern toolkits in ID. This review is the first of its kind in framing the development of biokinetic solving methods as the juxtaposition of mathematical solving schemes and computational capabilities, highlighting the evolution in biokinetic solving for radiation dose assessment.

This study compares the accuracy, efficiency, and reliability of variance reduction (VR) methods for Monte Carlo radiation transport simulations involving wide-area ground plane (i.e., “surface”) and buried (i.e., “volumetric”) gamma source emissions from environmental soil. The simulation models are idealized external exposure scenarios intended as a basis for deriving site-specific dose-based or carcinogenic risk–based regulatory limits in the radiological site remediation process. These simulations are computationally resource intensive since particle tracks are transported from an extremely large source region to a relatively small detector region. For each simulation, several VR methods are compared with metrics of accuracy, efficiency, and reliability. The MCNP deterministic transport (DXTRAN) VR method was most effective for problems involving sources emitting low-energy gamma rays, and a coupled multicode method was more effective for problems involving sources emitting higher-energy gamma rays that undergo significant attenuation in the soil.

Current nuclear facility emergency planning zones (EPZs) are based on outdated distance-based criteria, predating comprehensive dose and risk-informed frameworks. Recent advancements in simulation tools have permitted the development of site-specific, dose, and risk-based consequence-driven assessment frameworks. This study investigated the computation of advanced reactor (AR) EPZs using two atmospheric dispersion models: a straight-line Gaussian plume model (GPM) and a semi-Lagrangian Particle in Cell (PIC). Two case studies were conducted: (1) benchmarking the NRC SOARCA study for the Peach Bottom Nuclear Generating Station and (2) analyzing an advanced INL Heat Pipe Design A microreactor's end-of-cycle inventory. The dose criteria for both cases were 10 mSv at mean weather conditions and 50 mSv at 95th percentile weather conditions at 96 h post-release. Results demonstrated that GPM and PIC estimated similar mean peak dose levels for large boiling water reactors in the farfield case, placing EPZ limits beyond current regulations. For ARs with source terms remaining in the nearfield, PIC modeling without specific nearfield considerations could result in excessively high doses and inaccurate EPZ designations. PIC dispersion demonstrated an order of magnitude higher estimate of nearfield inhalation dose contribution when compared to GPM results. Both models significantly reduced EPZ sizing within the nearfield. Thus, reductions in the AR source term may eliminate the need for a separate EPZ.

During a radiological or nuclear emergency, occupational workers, members of the public, and emergency responders may be exposed to radionuclides, whether external or internal, through inhalation, ingestion, or wounds. In the case of internalized radiation exposure, prompt assessment of contamination is necessary to inform subsequent medical interventions. This review assembles the constituent considerations for managing nuclear and radiological incidents, focused on a parallel analysis of the evolution of radiation dose limits - notably in the emergency preparedness and response realm - alongside a discussion of triage systems and in vivo radionuclide detection tools. The review maps the development of international and national standards and regulations concerning radiation dose limits, illuminating how past incidents and accumulated knowledge have informed present emergency preparedness and response practices, specifically for internalized radiation. Additionally, the objectives and levels of radiation triage systems are explored in-depth, along with a global survey of practices and protocols. Finally, this review also focuses on in vivo detection systems and their capacities for radionuclide identification, prioritizing internalized gamma-emitting isotopes due to their broader relevance. Collectively, this study comprehensively addresses the intricacies of triage management following radiation emergencies, emphasizing the imperative for enhanced standardization and continued research in this critical domain.

Abstract In biokinetic modeling systems employed for radiation protection, biological retention and excretion have been modeled as a series of discretized compartments representing the organs and tissues of the human body. Fractional retention and excretion in these organ and tissue systems have been mathematically governed by a series of coupled first-order ordinary differential equations (ODEs). The coupled ODE systems comprising the biokinetic models are usually stiff due to the severe difference between rapid and slow transfers between compartments. In this study, the capabilities of solving a complex coupled system of ODEs for biokinetic modeling were evaluated by comparing different Python programming language solvers and solving methods with the motivation of establishing a framework that enables multi-level analysis. The stability of the solvers was analyzed to select the best performers for solving the biokinetic problems. A Python-based linear algebraic method was also explored to examine how the numerical methods deviated from an analytical or semi-analytical method. Results demonstrated that customized implicit methods resulted in an enhanced stable solution for the inhaled 60 Co (Type M) and 131 I (Type F) exposure scenarios for the inhalation pathway of the International Commission on Radiological Protection (ICRP) Publication 130 Human Respiratory Tract Model (HRTM). The customized implementation of the Python-based implicit solvers resulted in approximately consistent solutions with the Python-based matrix exponential method ( expm ). The differences generally observed between the implicit solvers and expm are attributable to numerical precision and the order of numerical approximation of the numerical solvers. This study provides the first analysis of a list of Python ODE solvers and methods by comparing their usage for solving biokinetic models using the ICRP Publication 130 HRTM and provides a framework for the selection of the most appropriate ODE solvers and methods in Python language to implement for modeling the distribution of internal radioactivity.

ABSTRACT: Differentiated thyroid cancer (DTC) is commonly treated first with a partial or complete thyroidectomy, followed by radioiodine (RAI) ablative therapy to eliminate remaining cancer cells. In such treatments, physical decay and urinary excretion are the primary means of 131 I. As such, patients with impaired urinary ability clearance, such as patients with end-stage renal disease (ESRD) whose urinary ability is impaired by dysfunction, can retain abnormally high activities of RAI, posing a concern to both the patient and those with whom the patient interacts. Additionally, ESRD patients are commonly administered dialysis therapy, wherein their blood is externally cycled through a dialyzer (hemodialysis) or filtered by instilling a dialysate fluid into the peritoneum (peritoneal dialysis) to filter uremic toxins from their blood that accumulate due to kidney dysfunction. These factors make determining release and dosing for ESRD patients receiving RAI therapy dependent on a plurality of variables. An evaluation of the current patient release guidelines, as given in US Nuclear Regulatory Commission (US NRC) Regulatory Guide 8.39 Rev. 1 for ESRD patients receiving RAI, has yet to be addressed. In this study, a biokinetic model for 131 I in ESRD patients receiving dialysis has been developed, improving on traditional two-compartment models, reflective of kinetics from multi-compartment models with updated transfer coefficients modified to reflect the different physiological functions of compartments. This updated biokinetic model was integrated with Monte Carlo radiation transport calculations using stylized computational hermaphroditic phantoms to calculate dose rate coefficients in exposure scenarios and compared with those of the point source models of NRC Reg Guide 8.39 Rev. 1 (and the proposed verbiage in Rev. 2). Results demonstrated that the baseline models of Rev. 1 and Rev. 2 overestimated the effective dose rate to an exposed individual for the majority of time post-administration, where both models overestimated the total dose to the maximally exposed individual. However, the application of several patient-specific modifying factors to the Rev. 2 model resulted in an overestimation by only a factor of 1.25, and in general, the results produced with the patient-specific modifications provide improved convergence with the dose rate coefficients computed in this study for ESRD patients.

Abstract Characterizing the interplay between exposures shaping the human exposome is vital for disease etiology. For example, cancer incidence is attributable to the independent and interactive multifactorial external exposures that shape health. Lung cancer is a perfect example of increased risk linked to environmental, socioeconomic, and lifestyle factors. However, radon epidemiological studies often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross activity and indoor radon) and PM 2.5 on lung cancer rates in the United States. The county-level factors (environmental, sociodemographic, lifestyle) were controlled, and Poisson regression and random forest were used to assess associations with lung and bronchus cancer rates. Tree-based ML method improved over traditional regression: Poisson regression: 7.58/7.39 (mean absolute percentage error, MAPE); Poisson random forest regression: 1.21/1.16 (MAPE). Effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to include multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of environmental radiation exposure and lung cancer incidence.

ABSTRACT: A preclinical radiotherapy system producing FLASH dose rates with 12 MV bremsstrahlung x rays is being developed at Stanford University and SLAC National Accelerator Laboratory. Because of the high expected workload of 6,800 Gy w -1 at the isocenter, an efficient shielding methodology is needed to protect operators and the public while the preclinical system is operated in a radiation therapy vault designed for 6 MV x rays. In this study, an analysis is performed to assess the shielding of the local treatment head and radiation vault using the Monte Carlo code FLUKA and the empirical methodology given in the National Council on Radiation Protection and Measurements Report 151. Two different treatment head shielding designs were created to compare single-layer and multilayer shielding methodologies using high-Z and low-Z materials. The multilayered shielding methodology produced designs with a 17% reduction in neutron fluence leaking from the treatment head compared to the single layered design of the same size, resulting in a decreased effective dose to operators and the public. The conservative assumptions used in the empirical methods can lead to over-shielding when treatment heads use polyethylene or multilayered shielding. High-Z/Low-Z multilayered shielding optimized via Monte Carlo is shown to be effective in the case of treatment head shielding and provide more effective shielding design for external beam radiotherapy systems that use 12 MV bremsstrahlung photons. Modifications to empirical methods used in the assessment of MV radiotherapy systems may be warranted to capture the effects of polyethylene in treatment head shielding.

The goal of this meeting was to draft a whitepaper on prioritized nuclear data covariance and uncertainty quantification needs impacting users for the next 5 to 10 years. These needs are described herein in an actionable context (i.e., a high-level plan is given to address them), and are feasible for the community to tackle the need (i.e., high-level idea of funding is provided). It should be noted that each of these proposed projects are ideal for training new nuclear data evaluators who are also integrated into application needs.

Dosimetric calculations, whether for radiation protection or nuclear medicine applications, are greatly influenced by the use of computational models of humans, called anthropomorphic phantoms. As anatomical models of phantoms have evolved and expanded, thus has the need for quantifying differences among each of these representations that yield variations in organ dose coefficients, whether from external radiation sources or internal emitters. This work represents an extension of previous efforts to quantify the differences in organ positioning within the body between a stylized and voxel phantom series. Where prior work focused on the organ depth distribution vis-à-vis the surface of the phantom models, the work described here quantifies the intra-organ and inter-organ distributions through calculation of the mean chord lengths. The revised Oak Ridge National Laboratory stylized phantom series and the University of Florida/National Cancer Institute voxel phantom series including a newborn, 1-, 5-, 10- and 15 year old, and adult phantoms were compared. Organ distances in the stylized phantoms were computed using a ray-tracing technique available through Monte Carlo radiation transport simulations in MCNP6. Organ distances in the voxel phantom were found using phantom matrix manipulation. Quantification of differences in organ chord lengths between the phantom series displayed that the organs of the stylized phantom series are typically situated farther away from one another than within the voxel phantom series. The impact of this work was to characterize the intra-organ and inter-organ distributions to explain the variations in updated internal dose coefficient quantities (i.e. specific absorbed fractions) while providing relevant data defining the spatial and volumetric organ distributions in the phantoms for use in subsequent internal dosimetric computations, with prospective relevance to patient-specific individualized dosimetry, as well as informing machine learning definition of organs using these reference models.

PURPOSE: A preclinical MV-energy photon FLASH radiotherapy system is being designed at Stanford and SLAC National Accelerator Laboratory. Because of the higher energy and dose rate compared to conventional kV-energy photon laboratory-scale irradiators, adequate shielding in a stand-alone cabinet form factor is more challenging to achieve. We present a Monte Carlo simulation of multilayered shielding for a compact self-shielding system without the need for a radiation therapy vault. METHODS: A multilayered shielding approach using multiple alternating layers of high-Z and low-Z materials is applied to the self-shielded cabinet to effectively mitigate the secondary radiation produced and to allow the device to be housed in a Controlled Radiation Area outside of a radiation vault. The multilayered shielding approach takes advantage of the properties of high-Z and low-Z radiation shielding materials such as density, cross-section, atomic number of the shielding elements, and products of radiation interactions within each layer. The Monte Carlo radiation transport code, FLUKA, is used to simulate the total effective dose produced by the operation. RESULTS: The multilayered shielding designs proposed and simulated produced effective dose rates significantly lower than monolayer designs with the same total material thickness at the regulatory boundary; this is accomplished through the manipulation of the locations where secondary radiation is produced and reactions due to material properties such as neutron back reflection in hydrogen. Borated polyethylene at 5 wt% significantly increased the shielding performance as compared to regular polyethylene, with the magnitude of the reduction depending upon the order of the shielding material. CONCLUSIONS: The multilayered shielding provides a path for shielding preclinical FLASH systems that deliver MV-energy bremsstrahlung photons. This approach promises to be more efficient with respect to the shielding material mass and space claim as compared to shielded vaults typically required for clinical radiation therapy with MV photons.