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Radiation-Induced Lens Opacities among Interventional Cardiologists: Retrospective Assessment of Cumulative Eye Lens Doses
This study describes the retrospective lens dose calculation methods developed and applied within the European epidemiological study on radiation-induced lens opacities among interventional cardiologists. While one approach focuses on self-reported data regarding working practice in combination with available procedure-specific eye lens dose values, the second approach focuses on the conversion of the individual whole-body dose to eye lens dose. In contrast with usual dose reconstruction methods within an epidemiological study, a protocol is applied resulting in an individual distribution of possible cumulative lens doses for each recruited cardiologist, rather than a single dose estimate. In this way, the uncertainty in the dose estimate (from measurement uncertainty and variability among cardiologists) is represented for each individual. Eye lens dose and whole-body dose measurements have been performed in clinical practice to validate both methods, and it was concluded that both produce acceptable results in the framework of a dose-risk evaluation study. Optimal results were obtained for the dose to the left eye using procedure-specific lens dose data in combination with information collected on working practice. This method has been applied to 421 interventional cardiologists resulting in a median cumulative eye lens dose of 15.1 cSv for the left eye and 11.4 cSv for the right eye. From the individual cumulative eye lens dose distributions obtained for each cardiologist, maxima up to 9-10 Sv were observed, although with low probability. Since whole-body dose values above the lead apron are available for only a small fraction of the cohort and in many cases not for the entire working career, the second method has only been used to benchmark the results from the first approach. This study succeeded in improving the retrospective calculation of cumulative eye lens doses in the framework of radiation-induced risk assessment of lens opacities, but it remains dependent on self-reported information, which is not always reliable for early years. However, the calculation tools developed can also be used to make an assessment of the eye lens dose in current practice.
Big-Data Multi-Energy Iterative Volumetric Reconstruction Methods for As-Built Validation & Verification Applications
This document archives the results developed by the Lab Directed Research and Development (LDRD) project sponsored by Sandia National Laboratories (SNL). In this work, it is shown that SNL has developed the first known high-energy hyperspectral computed tomography system for industrial and security applications. The main results gained from this work include dramatic beam-hardening artifact reduction by using the hyperspectral reconstruction as a bandpass filter without the need for any other computation or pre-processing; additionally, this work demonstrated the ability to use supervised and unsupervised learning methods on the hyperspectral reconstruction data for the application of materials characterization and identification which is not possible using traditional computed tomography systems or approaches.
Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data
Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.
Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks
Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.
Lost and Found Opportunities Around the Chlorine Worth Study
Los Alamos National Laboratory performed a series of critical experiments in 2021 to examine the worth of chlorine in plutonium-fueled systems. This series of experiments has been dubbed the “Chlorine Worth Study,” and the evaluation of the experiments was presented to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Technical Review Group in April, 2023. The primary purpose of these experiments was to enable validation of aqueous solutions crediting neutron absorption in 35 Cl. An external, independent view of the events leading up to the design and execution of these experiments indicates a missed opportunity to leverage sensitivity/uncertainty (S/U) analysis to assert validation without the experiments by taking an additional margin for the lack of direct validation of chlorine. On the other hand, the execution of these experiments also presents a rare opportunity to examine the efficacy of the S/U approach and extract useful information about the evaluated chlorine covariance data. TSUNAMI-1D models of representative application solutions were created and used to generate sensitivity data. Varying plutonium and chlorine concentrations were considered to examine the impact of these differences on the chlorine sensitivities and uncertainties. The data-induced uncertainty in k eff resulting from chlorine was calculated directly from uncertainty information calculated in the TSUNAMI-1D sequence. In all cases, this uncertainty was less than 0.1 %Δk. This result could potentially be used to justify a reactivity margin to account for the validation gap related to chlorine in the validation set. On the other hand, given that the experiments were performed, the community should endeavor to extract as much value from them and their results as possible. The results can be used to examine the actual bias associated with chlorine in these systems once the evaluations have been released. These data can be compared with the data-induced uncertainty margin discussed above to test the sufficiency of the validation gap penalty. This result will provide an indication of the performance of the chlorine covariance data specifically and the S/U validation approach generally. More advanced S/U techniques may also be employed to determine reactivity sensitivities associated with the chlorine in the experiments, potentially generating a more robust test of the chlorine covariance data.
Evolving Applications of Aerosol Sealant
Solving the issues facing the residential building industry today requires developing not only new technologies but also new, innovative applications of existing high-performance technologies. The U.S. Department of Energy's (DOE's) Building America Program is using this approach to develop a low-cost, easy solution for whole-home envelope air sealing. Aerosol sealant is a proven method for sealing ducts quickly and efficiently, especially in hard-to-reach places. This technology, originally developed at Lawrence Berkeley National Laboratory in 1994 with support from DOE, seals 70%-90% of duct leaks (LBNL n.d.), saving $600-$850 per year in energy costs for an average household (DOE 2011). A new, innovative application takes this technology out of the duct system and into the conditioned space. Recent Building America projects have validated a method for whole-house air sealing using the aerosol sealant technology. With tighter envelope requirements in new codes and above-code programs, adequately air sealing by hand is more labor-intensive and costly than ever. The aerosol sealing method is designed to eliminate the guesswork and automate this process. The continuous measurement during installation allows builders to dial in the exact air tightness they need to meet their performance goals. Initial field testing shows that this method regularly achieves air leakage levels less than 1 ACH50 as measured by a blower door test. In a current Building America project, the Center for Energy and Environment is performing iterative field tests in 20 homes to identify the optimal integration and sequencing of this method in production home building processes to achieve a lower installation cost than traditional air sealing. This method has the potential to improve quality assurance of envelope sealing and significantly reduce labor costs compared with traditional air sealing approaches.
Dual-Channel Enzymatic Inhibition Measurement (DEIM) Coupling Isotope Substrate via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry
A novel dual-channel enzymatic inhibition measurement (DEIM) method was developed to improve the repeatability with light/heavy isotope substrates, producing reliable relative standard deviations (< 3%) by employing acetylcholinesterase (AChE) as the model enzyme. The matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) was adapted for enzyme-inhibited method due to its good salt-tolerance and high throughput; meanwhile, dual-channel enzymatic reactions were performed to improve the repeatability of each well. The acetylcholinesterase inhibition measurement was conducted by mixing the quenched enzyme reaction solution of blank group (with heavy isotope as substrate) and experimental group (with light isotope as substrate), of which the inhibition rate might be affected by isotope effects. Hence, inverse study and Km measurement were implemented to validate the method. The inverse study shows similar inhibition rate (68.9 and 70.3%) and the Km of isotope substrates are analogous (0.139 and 0.135 mM), which demonstrated that the novel method is feasible to AChE inhibition measurement. Finally, the method was applied to herb extracts, half of which exhibit inhibition to AChE. The precise dual-channel enzymatic inhibition measurement (DEIM) method could be regarded as a promising approach to potential enzyme inhibitor screening. .
Physics-Based Machine Learning Methods for U-235 Forensics Signatures
Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.
Physics-Based Machine Learning Methods for U-235 Forensics Signatures
Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.
Reconstruction of Galaxy Star Formation Histories through SED Fitting:The Dense Basis Approach
We introduce the dense basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M {sub *}, SFR, and dust attenuation, and reveals previously inaccessible information about the number and duration of star formation episodes and the timing of stellar mass assembly, as well as uncertainties in these quantities. This is done using basis star formation histories (SFHs) chosen by comparing the goodness-of-fit of mock galaxy SEDs to the goodness-of-reconstruction of their SFHs. We train and validate the method using a sample of realistic SFHs at z = 1 drawn from stochastic realizations, semi-analytic models, and a cosmological hydrodynamical galaxy formation simulation. The method is then applied to a sample of 1100 CANDELS GOODS-S galaxies at 1 10{sup 9.5} M{sub ⊙}. About 40% of the CANDELS galaxies have SFHs whose maximum occurs at or near the epoch of observation. The dense basis method is scalable and offers a general approach to a broad class of data-science problems.
SU-C-207A-05: Feature Based Water Equivalent Path Length (WEPL) Determination for Proton Radiography by the Technique of Time Resolved Dose Measurement
Purpose: Studies show that WEPL can be determined from modulated dose rate functions (DRF). However, the previous calibration method based on statistics of the DRF is sensitive to energy mixing of protons due to scattering through different materials (termed as range mixing here), causing inaccuracies in the determination of WEPL. This study intends to explore time-domain features of the DRF to reduce the effect of range mixing in proton radiography (pRG) by this technique. Methods: An amorphous silicon flat panel (PaxScan™ 4030CB, Varian Medical Systems, Inc., Palo Alto, CA) was placed behind phantoms to measure DRFs from a proton beam modulated by a specially designed modulator wheel. The performance of two methods, the previously used method based on the root mean square (RMS) and the new approach based on time-domain features of the DRF, are compared for retrieving WEPL and RSP from pRG of a Gammex phantom. Results: Calibration by T{sub 80} (the time point for 80% of the major peak) was more robust to range mixing and produced WEPL with improved accuracy. The error of RSP was reduced from 8.2% to 1.7% for lung equivalent material, with the mean error for all other materials reduced from 1.2% to 0.7%. The mean error of the full width at half maximum (FWHM) of retrieved inserts was decreased from 25.85% to 5.89% for the RMS and T{sub 80} method respectively. Monte Carlo simulations in simplified cases also demonstrated that the T{sub 80} method is less sensitive to range mixing than the RMS method. Conclusion: WEPL images have been retrieved based on single flat panel measured DRFs, with inaccuracies reduced by exploiting time-domain features as the calibration parameter. The T{sub 80} method is validated to be less sensitive to range mixing and can thus retrieve the WEPL values in proximity of interfaces with improved numerical and spatial accuracy for proton radiography.
TU-D-209-02: A Backscatter Point Spread Function for Entrance Skin Dose Determination
Purpose: To determine the distribution of backscattered radiation to the skin resulting from a non-uniform distribution of primary radiation through convolution with a backscatter point spread function (PSF). Methods: A backscatter PSF is determined using Monte Carlo simulation of a 1 mm primary beam incident on a 30 × 30 cm × 20 cm thick PMMA phantom using EGSnrc software. A primary profile is similarly obtained without the phantom and the difference from the total provides the backscatter profile. This scatter PSF characterizes the backscatter spread for a “point” primary interaction and can be convolved with the entrance primary dose distribution to obtain the total entrance skin dose. The backscatter PSF was integrated into the skin dose tracking system (DTS), a graphical utility for displaying the color-coded skin dose distribution on a 3D graphic of the patient during interventional fluoroscopic procedures. The backscatter convolution method was validated for the non-uniform beam resulting from the use of an ROI attenuator. The ROI attenuator is a copper sheet with about 20% primary transmission (0.7 mm thick) containing a circular aperture; this attenuator is placed in the beam to reduce dose in the periphery while maintaining full dose in the region of interest. The DTS calculated primary plus backscatter distribution is compared to that measured with GafChromic film and that calculated using EGSnrc Monte-Carlo software. Results: The PSF convolution method used in the DTS software was able to account for the spread of backscatter from the ROI region to the region under the attenuator. The skin dose distribution determined using DTS with the ROI attenuator was in good agreement with the distributions measured with Gafchromic film and determined by Monte Carlo simulation Conclusion: The PSF convolution technique provides an accurate alternative for entrance skin dose determination with non-uniform primary x-ray beams. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.
Characterizing post-fire delayed tree mortality with remote sensing: sizing up the elephant in the room
Abstract Background Despite recent advances in understanding the drivers of tree-level delayed mortality, we lack a method for mapping delayed mortality at landscape and regional scales. Consequently, the extent, magnitude, and effects of delayed mortality on post-fire landscape patterns of burn severity are unknown. We introduce a remote sensing approach for mapping delayed mortality based on post-fire decline in the normalized burn ratio (NBR). NBR decline is defined as the change in NBR between the first post-fire measurement and the minimum NBR value up to 5 years post-fire for each pixel. We validate the method with high-resolution aerial photography from six wildfires in California, Oregon, and Washington, USA, and then compare the extent, magnitude, and effects of delayed mortality on landscape patterns of burn severity among fires and forest types. Results NBR decline was significantly correlated with post-fire canopy mortality (r 2 = 0.50) and predicted the presence of delayed mortality with 83% accuracy based on a threshold of 105 NBR decline. Plots with NBR decline greater than 105 were 23 times more likely to experience delayed mortality than those below the threshold (p < 0.001). Delayed mortality occurred across 6–38% of fire perimeters not affected by stand-replacing fire, generally affecting more areas in cold (22–41%) and wet (30%) forest types than in dry (1.7–19%) types. The total area initially mapped as unburned/very low-severity declined an average of 38.1% and generally persisted in smaller, more fragmented patches when considering delayed mortality. The total area initially mapped as high-severity increased an average of 16.2% and shifted towards larger, more contiguous patches. Conclusions Differences between 1- and 5-year post-fire burn severity maps depict dynamic post-fire mosaics resulting from delayed mortality, with variability among fires reflecting a range of potential drivers. We demonstrate that tree-level delayed mortality scales up to alter higher-level landscape patterns of burn severity with important implications for forest resilience and a range of fire-driven ecological outcomes. Our method can complement existing tree-level studies on drivers of delayed mortality, refine mapping of fire refugia, inform estimates of habitat and carbon losses, and provide a more comprehensive assessment of landscape and regional scale fire effects and trends.
Fast Learning for Immersive Engagement in Energy Simulations
The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.
Coherent amplification of X-ray scattering from meso-structures
Small-angle X-ray scattering (SAXS) often includes an unwanted background, which increases the required measurement time to resolve the sample structure. This is undesirable in all experiments, and may make measurement of dynamic or radiation-sensitive samples impossible. Here, we demonstrate a new technique, applicable when the scattering signal is background-dominated, which reduces the requisite exposure time. Our method consists of exploiting coherent interference between a sample with a designed strongly scattering `amplifier'. A modified angular correlation function is used to extract the symmetry of the interference term; that is, the scattering arising from the interference between the amplifier and the sample. This enables reconstruction of the sample's symmetry, despite the sample scattering itself being well below the intensity of background scattering. Thus, coherent amplification is used to generate a strong scattering term (well above background), from which sample scattering is inferred. We validate this method using lithographically defined test samples.
A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids
We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Here, our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long–wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.
Dry cask radiation shielding validation and estimation of cask surface dose rate with MAVRIC during long-term storage
The dry storage cask became a semi-permanent solution for spent nuclear fuel since no permanent solution has been finalized over the past three decades. Considering the difficulty of deep geological repository projects in many aspects, prolonging the service life of dry casks longer than a few decades has become an option for spent nuclear fuel storage. It must be ensured that dry cask safety objectives, including radiation shielding, criticality safety, structure health, decay heat removal, and containment be fulfilled during the prolonged service life. This paper presents numerical modeling results of the radiation dose rate distribution on a TN-32 cask surface aged up to 300 years. The numerical modeling method was validated with measurement data, and the difference between the calculated results and measurement data in the total dose rate is 7%. The detailed radiation distribution variations over cask surfaces along the axial and radial directions for 300 years of storage were obtained, and their azimuthal angular dependence was established. The maximum total dose rate over the cask surfaces is less than 10 mrem/hour (9.258 ± 0.212 mrem/hour) after 40 years, and this outcome fulfills all dose rate requirements from 10 CFR 71.47(b). After 300 years of storage, the average total dose rates on the side surface and top surface are 0.150 ± 0.007mremmrem/hour and 0.057 ± 0.003mremmrem/hour, respectively, while the maxima are 0.601 ± 0.014 mrem/hour and 0.112 ± 0.006 mrem/hour, respectively. The variations in the fuel gamma ray, structure gamma ray, neutron and secondary gamma-ray contributions to the total dose rate are presented and analyzed in this work. The neutron and gamma-ray dose rate contributions by energy group are presented and analyzed at the cask top, mid-plane, and bottom at the selected storage times.