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DOE OSTI2017

Effect of crack openings on carbonation-induced corrosion

Reinforced concrete is widely used in the construction of buildings, historical monuments, infrastructures and nuclear power plants. For a variety of reasons, many concrete structures are subject to unavoidable cracks that accelerate the diffusion of atmospheric carbon dioxide to the steel/concrete interface. Carbonation at the interface induces steel corrosion that could cause the development of new cracks in the structure, a determining factor for its durability. The aim of this article is to study the effect of existing cracks on the development of carbonation-induced corrosion. The results indicate that, after the initiation phase, the corrosion kinetics decreases with time and the free corrosion potential increases independently of the crack opening. In addition, the corroded zone matches the carbonated one. The interpretation of these results allows the authors to conclude that, during the corrosion process, corrosion products seal the crack and act as a barrier to oxygen and water diffusion. Consequently, the influence of crack opening on corrosion development is masked and the corrosion development is limited.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Applying image processing methods to study hydrodynamic characteristics in a rectangular spouted bed

This paper presents systematic image processing methods for investigating hydrodynamic characteristics in a rectangular spouted bed (30.2 mm × 101.6 mm). The objective is to provide a non-intrusive technique to determine the bed expansion ratio, the jet height and the fountain height due to their significant influence on the hydrodynamics of the entire bed. A high-speed video camera is used in the experiment with Geldart D particles as the bed material. After the image acquisition, image denoising, thresholding, logical and mask operations are applied correspondingly for the boundary discrimination, based on which an algorithm has been developed to determine the bed height successfully. With the discriminated boundary, morphological operations viz. erosion and dilation have been used to accurately determine the jet height and the fountain height. The feasibility of the technique is proven through profiles of bed expansion ratio, jet height and fountain height under three initial bed heights (0.0762 m, 0.102 m, and 0.127 m) for two different nozzle sizes (0.0096 m and 0.0127 m). Depending on the determination of the hydrodynamic characteristics, different flow regimes can be distinguished comprehensively through profiles of the bed expansion ratio and bed pressure drop in combination with the standard deviation of jet/fountain height. The proposed flow regime discrimination verifies the feasibility and effectiveness of the image processing methods in assessing the solids behavior in the spouted beds. Results about the variation of the jet height and the fountain height under current operating conditions as determined by the new technique are also discussed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

GMRES with embedded ensemble propagation for the efficient solution of parametric linear systems in uncertainty quantification of computational models

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. Furthermore, this example demonstrates improved ensemble propagation speed-up without reduction.

BLAS↗

DOE OSTI2023

A deep learning model for automatic analysis of cavities in irradiated materials

Transmission electron microscopy (TEM) is a commonly used technique in materials science for defect investigation. Quantitative analysis of defects is important for understanding the properties of a material, but manual analysis of TEM micrographs can be time-consuming and prone to error, especially when the defects have irregular shapes rather than spherical shapes. Many existing methods or deep learning models do not handle a wide range of sizes for the same object type within a single image. In this work, we present a framework that enables users to train an instance segmentation model called Mask R- CNN on any microstructure dataset, perform multi-detection on the same image at different scales, and obtain properties (e.g., size, area) of the objects based on the desired shape (e.g., circle, ellipse, rectangle). Additionally, we have developed a parallel detection module that uses multiple GPUs to increase the efficiency of the object detection process. We demonstrate the capabilities of our framework using a set of TEM images of cavities with different shapes, size distributions, and background contrasts. Finally, we show that the performance of our model in terms of density, size, and swelling of the cavities is comparable to the human average and that our model achieves the highest recall value compared to existing methods due to the use of image multi-rescaling.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Editorial overview: Unlocking the secrets of nongenetic plasticity, one cell at a time

Cellular noise, the non-genetic variability observed among isogenic cells, arises from factors such as growth conditions, aging, and stochastic gene expression, influencing cell stress-response, metabolism, morphology, and size. Here, such plasticity, while critical for adaptation, often goes unnoticed with traditional population-averaging biotechnologies that inevitably mask cell-specific variations and prompting the question, "What else might we be missing". However, recent breakthroughs in optical imaging, microfluidics, and omics, are beginning to uncover the complexity of cellular plasticity. This special is-sue highlights some of these breakthroughs, with key contributions including innovations in multimodal chemical imaging, label-free microscopy, spatial and temporal omics, and droplet-based microfluidics. Collectively, these cutting-edge tools provide unprecedent-ed insights into non-genetic cell-to-cell variability, enhancing our understanding of cellu-lar plasticity and its implications for health, energy, and ecology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Comment on “Characterizing the population of pulsars in the Galactic bulge with the Fermi large area telescope” [arXiv:1705.00009v1]

Here, the $\textit{Fermi}$-LAT Collaboration recently presented a new catalog of gamma-ray sources located within the $40^{\circ} \times 40^{\circ}$ region around the Galactic Center~(Ajello et al. 2017) -- the Second Fermi Inner Galaxy (2FIG) catalog. Utilizing this catalog, they analyzed models for the spatial distribution and luminosity function of sources with a pulsar-like gamma-ray spectrum. Ajello et al. 2017 v1 also claimed to detect, in addition to a disk-like population of pulsar-like sources, an approximately 7$\sigma$ preference for an additional centrally concentrated population of pulsar-like sources, which they referred to as a "Galactic Bulge" population. Such a population would be of great interest, as it would support a pulsar interpretation of the gamma-ray excess that has long been observed in this region. In an effort to further explore the implications of this new source catalog, we attempted to reproduce the results presented by the $\textit{Fermi}$-LAT Collaboration, but failed to do so. Mimicking as closely as possible the analysis techniques undertaken in Ajello et al. 2017, we instead find that our likelihood analysis favors a very different spatial distribution and luminosity function for these sources. Most notably, our results do not exhibit a strong preference for a "Galactic Bulge" population of pulsars. Furthermore, we find that masking the regions immediately surrounding each of the 2FIG pulsar candidates does $\textit{not}$ significantly impact the spectrum or intensity of the Galactic Center gamma-ray excess. Although these results refute the claim of strong evidence for a centrally concentrated pulsar population presented in Ajello et al. 2017, they neither rule out nor provide support for the possibility that the Galactic Center excess is generated by a population of low-luminosity and currently largely unobserved pulsars.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗

DOE OSTI2021

Effect of Pressure and Temperature on Carbon Dioxide Reduction at a Plasmonically Active Silver Cathode

Here, carbon dioxide reduction at a plasmonically active silver cathode was investigated by varying the pressure and temperature at multiple applied potentials under both dark and illuminated conditions to understand the mechanism of selectivity changes driven by plasmon-enhanced electrochemical conversion. Carbon dioxide partial pressures (P Co 2 ) from 0.2 to 1 atm were studied during linear sweep voltammetry and chronoamperometry at -0.7, -0.9, and -1.1 V RHE . At a given applied overpotential the total current density increased with increasing P Co 2 in both the dark and the light, but there were significant differences in the Tafel behavior between dark and illuminated conditions. The reduction of CO 2 to carbon monoxide (CO) was found to have first-order behavior with respect to P Co 2 at all applied potentials in both the dark and the light, likely indicating no change in the rate-determining step upon illumination. Activity for the hydrogen (H 2 ) evolution reaction decreased with increasing P Co 2 at slightly different rates in the dark and the light at each applied potential, making it unclear if light is influencing CO or H 2 intermediate adsorbate coverage. Both formate and methanol production showed no dependence on P Co 2 under any conditions, but the true reaction orders may be masked by the much higher activity for CO and H 2 at the silver cathode. The investigation of product distribution with temperature at 14, 22, and 32°C at -0.7, -0.9, and -1.1 V RHE in both the dark and the light demonstrated that the selectivity changes observed upon illumination are not caused by local heating of the cathode surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Verifying infectious disease scenario planning for geographically diverse populations

In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model. We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations. Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Stratigraphic control of landscape response to base-level fall, Young Womans Creek, Pennsylvania, USA [Caprock controls on landscape response to baselevel fall constrained by nested detrital in situ 10Be, Young Womans Creek, Pennsylvania, USA]

Landscapes are thought to respond to changes in relative base level through the upstream propagation of a boundary that delineates relict from adjusting topography. However, spatially-variable rock strength can influence the topographic expression of such transient landscapes, especially in layered rocks, where strength variations can mask topographic signals expected due to changes in climate or tectonics. Here, we analyze the landscape response to base-level fall in Young Womans Creek, a 220 km 2 catchment on the Appalachian Plateau, USA underlain by gently folded Paleozoic sedimentary rocks. We measured in situ 10 Be concentrations in stream sands from 17 nested watersheds, and used a spatially-distributed model of sediment and 10 Be production to constrain a threefold increase in the rate of base-level fall propagating upstream from the catchment outlet. Using lidar topography and a nearby detailed stratigraphic section, we map the extent of continuous, blocky, resistant sandstone strata that act as a caprock overlying more easily erodible sandstones and siltstones. The caprock influences landscape response in two ways. First, it serves as a boundary between slowly eroding (11.5 m Myr –1 ), low-sloping (3–5°) areas of relict topography and lower, steeper portions of the landscape adjusting to base-level fall. Second, hillslopes supported by the overlying caprock are armored with coarse sediment and are significantly steeper (20–30°) than hillslopes where the caprock has been eroded (10°), despite having similar erosion rates (36 m Myr –1 ) and bedrock substrate. Lastly, our results illustrate how gently dipping, layered rocks engender complicated relationships between lithology, topography and erosion rate, highlighting the importance of understanding how rock material properties influence surface processes and landscape evolution.

58 GEOSCIENCES↗

DOE OSTI2023

The importance of capturing power system operational details in resource adequacy assessments

Traditional methods for assessing the resource adequacy (RA) of a power system are becoming obsolete due to emerging trends such as the increasing deployment of variable renewable energy and storage. Consequently, analysts are recommending that RA be assessed using a Monte Carlo simulation approach that models chronological power system operations over many instances of possible operating conditions. However, this approach is necessarily more complex and computationally demanding, which is an obstacle to real-world implementation. Here, in this study, we investigate which operational details of power systems are important to capture in order to accurately evaluate a system's RA, versus details that add complexity but do not meaningfully affect RA results. To do so, we develop a probabilistic RA assessment framework by adapting an existing production cost model and apply it to a case study based on the IEEE Reliability Test System. Our results indicate that multi-year data, storage dispatch, and transmission limits are key details to incorporate. Accurate RA results can be obtained using non-economic dispatch strategies as long as they are coordinated with detailed operational strategies. We also demonstrate how popular expectation-based RA metrics can mask important differences in the characteristics of loss of load events.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Activity model for 36 elements in Fe-Ni-Si-S-C liquids with application to terrestrial planet accretion and mantle geochemistry: New data for Ru, Re, Pt, Os, Ti, Nb, and Ta

Understanding siderophile element partitioning between metal and silicate melts under diverse conditions can be used to place important constraints on the materials and conditions of planetary accretion and core formation, as well as post core formation processes. However, the effects of Si on the partitioning and activity coefficients for these elements are not well known, despite Si likely being one of the dominant light elements in Earth’s core. To address this gap in understanding, we have undertaken a systematic study of the highly siderophile elements Re, Pt, Os, and Ru, and the refractory lithophile elements Nb, Ta and Ti at 1600 °C and 1 GPa, to derive epsilon interaction parameters for these elements in FeSi metallic liquids. Positive epsilon interaction parameters were measured for Nb, Ta, Ti, Ru, Re, Pt, and Os, indicating that dissolved Si in Fe liquids causes a decrease in their metal/silicate partition coefficients (or ‘silicophobic’ behavior). Furthermore, ε$_{Re, Os, or Ru}^{Si}$ > ε$_{Re, Os, or Ru}^{S}$ which means Si causes a larger decrease in D(metal/silicate) than S, and the chalcophile behavior expected from some elements will be completely masked by the presence of Si in a metallic liquid. The new parameters are used to update an activity model that now includes 36 siderophile elements in Fe-Ni-Si-S-C liquids (27 trace elements considered here). Systematic assessment of these 27 elements shows which have the strongest affinity for Si, C, and S, and also how activity coefficients for these elements would vary during accretion and core formation in Earth, Mars, and Mercury of widely differing fO 2 and core compositional conditions. The activity model is combined with new partitioning expressions for Mo, W, Cr, Re Ru, Pt, and Os and applied to aspects of post core formation mantle geochemistry of Earth, Mars, and Mercury. Our updated expressions show that the BSE Mo/W ratio can easily be achieved with metal/silicate partitioning during growth of the Earth, whereas Re, Os and Ru become lower than and highly fractionated compared with BSE values during core formation and accretion, and thus nearly 99% of their BSE abundances are likely contributed by late accretion. Ru isotopes should be a very good indicator of the source material for the late accretion. The high Pt/Os and Re/Os developed in a deepening magma ocean during the growth of the Earth, indicates 186 Os and 187 Os isotopes could be coupled if this ancient material remained isolated and subsequently became entrained in mantle plumes and measured in surficial lavas. The extent to which this occurred will be limited by the low Os content of this ancient material, thus requiring mixing as a major component in plume sources. Martian mantle Hf/W ratio stays low during accretion and core formation modelling, suggesting that W isotope anomalies are more likely due to solid/liquid silicate fractionation than to core formation. Finally, Ti contents measured by MESSENGER at Mercury’s surface can be explained by segregation of either a metallic core (IW-6 to -8) or metallic core + sulfide (IW-4 to -7.5) followed by mantle melting.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2018

Warming rate drives microbial nutrient demand and enzyme expression during peat decomposition

Recent developments of enzyme-based decomposition models highlight the importance of enzyme kinetics with warming, but most modeling exercises are based on studies with a step-wise warming. This approach may mask the effect of temperature in controlling in-situ activities as in most ecosystems the rate of warming is more gradual than these step warming studies. We conducted an experiment to test the effects of contrasting warming rates on the kinetics of carbon (C), nitrogen (N), and phosphorus (P) degradation enzymes in subtropical peat soils. We also wanted to evaluate if the stoichiometry of enzyme kinetics shifts under contrasting warming rates and if so, how does it relate to the stoichiometry in microbial biomass. Contrasting warming rates altered microbial biomass stoichiometry leading to differing patterns of microbial demand for C vs. nutrient (N and P) and enzyme expression following the optimum foraging strategy. Activity (higher Vmax) and efficiency (lower Km) of C acquisition enzymes were greater in the step treatment; however, expressions of nutrient (N and P) acquiring enzymes were enhanced in the ramp treatment at the end of the experiment. In the step treatment, there was a typical pattern of an initial peak in the Vmax and drop in the Km for all enzyme groups followed by later adjustments. On the other hand, a consistent increase in Vmax and decline in Km of all enzyme groups were observed in the ramp treatment. These changes were sufficient to alter microbial identity (as indicated by enzyme Km and biomass stoichiometry) with two apparently different endpoints under contrasting warming rates. This observation resembles the concept of alternate stable states and highlights a need for improved representation of warming effects on enzymes in decomposition models. Using peat soils of Florida Everglades, here we have demonstrated that contrasting warming rates can influence the dynamics of microbial and enzymatic kinetics. Hence, we suggest that future laboratory and field warming studies could consider our approach to accurately represent microbial and enzymatic kinetics in biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

The EGS Collab project: Outcomes and lessons learned from hydraulic fracture stimulations in crystalline rock at 1.25 and 1.5 km depth

With the goal of better understanding stimulation in crystalline rock for improving enhanced geothermal systems (EGS), the EGS Collab Project performed a series of stimulations and flow tests at 1.25 and 1.5 km depths. The tests were performed in two well-instrumented testbeds in the Sanford Underground Research Facility in Lead, South Dakota, United States. The testbed for Experiment 1 at 1.5 km depth contained two open wells for injection and production and six instrumented monitoring wells surrounding the targeted stimulation zone. Four multi-step stimulation tests targeting hydraulic fracturing and nearly year-long ambient temperature and chilled water flow tests were performed in Experiment 1. The testbed for Experiments 2 and 3 was at 1.25 km depth and contained five open wells in an outwardly fanning five-spot pattern and two fans of well-instrumented monitoring wells surrounding the targeted stimulation zone. Experiment 2 targeted shear stimulation, and Experiment 3 targeted low-flow, high-flow, and oscillating pressure stimulation strategies. Hydraulic fracturing was successful in Experiments 1 and 3 in generating a connected system wherein injected water could be collected. However, the resulting flow was distributed dynamically, and not entirely collected at the anticipated production well. Thermal breakthrough was not observed in the production well, but that could have been masked by the Joule-Thomson effect. Shear stimulation in Experiment 2 did not occur - despite attempting to pressurize the fractures most likely to shear - because of the inability to inject water into a mostly-healed fracture, and the low shear-to-normal stress ratio. The EGS Collab experiments are described to provide a background for lessons learned on topics including induced seismicity, the correlation between seismicity and permeability, distributed and dynamic flow systems, thermoelastic and pressure effects, shear stimulation, local geology, thermal breakthrough, monitoring stimulation, grouting boreholes, modeling, and system management.

15 - GEOTHERMAL ENERGY↗

DOE OSTI2026

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗

DOE OSTI2026

Regional inertia dynamics of U.S. interconnections: An event-based measurement approach

Power grid inertia plays a vital role in frequency stability following large disturbances, yet its distribution across the U.S. grid is highly uneven. While interconnection-wide inertia benchmarks are useful, they can mask regional variability driven by resource mix, network coupling, and geographic separation. This paper extends event-driven inertia estimation to the regional scale using field measurements from the Frequency Monitoring Network (FNET/GridEye). Starting from balancing authority and independent system operator footprints, candidate regions are refined using a composite coherency score that combines frequency-trajectory shape similarity, timing spread, and lead/lag behavior to ensure dynamic consistency. A filtered sliding difference method (FSDM) is then used to construct regional frequency trajectories, detect disturbance onset, and compute robust regional rate-of-change of frequency (RoCoF). Regional, local, and interconnection inertia are estimated by combining RoCoF with event power imbalance, and additional indicators (regional-to-system inertia ratio and inertial-support arrival time) quantify regional-to-interconnection coupling and relative regional contributions. The method is demonstrated on eleven regions across the Eastern Interconnection (EI) and the Western Electricity Coordinating Council (WECC), with the Electric Reliability Council of Texas (ERCOT) used for validation. In ERCOT, estimates compared against energy management system (EMS) values achieve a mean absolute percentage error of 17.94%. WECC exhibits consistently shorter inertial-support arrival times (0.15–0.3 s) than EI (0.7–1.1 s), highlighting contrasting coupling and disturbance-propagation behavior. Overall, the results reveal pronounced spatial heterogeneity in inertia and coupling, underscoring the value of regional monitoring for both operational decision-making and long-term system planning.

Disturbance events↗

DOE OSTI2014

Contrast-Enhanced Proton Radiography for Patient Set-up by Using X-Ray CT Prior Knowledge

Purpose: To obtain a contrasted image of the tumor region during the setup for proton therapy in lung patients, by using proton radiography and x-ray computed tomography (CT) prior knowledge. Methods and Materials: Six lung cancer patients' CT scans were preprocessed by masking out the gross tumor volume (GTV), and digitally reconstructed radiographs along the planned beam's eye view (BEV) were generated, for a total of 27 projections. Proton radiographies (PR) were also computed for the same BEV through Monte Carlo simulations. The digitally reconstructed radiograph was subtracted from the corresponding proton image, resulting in a contrast-enhanced proton radiography (CEPR). Michelson contrast analysis was performed both on PR and CEPR. The tumor region was then automatically segmented on CEPR and compared to the ground truth (GT) provided by physicians in terms of Dice coefficient, accuracy, precision, sensitivity, and specificity. Results: Contrast on CEPR was, on average, 4 times better than on PR. For 10 lateral projections (±45° off of 90° or 270°), although it was not possible to distinguish the tumor region in the PR, CEPR offers excellent GTV visibility. The median ± quartile values of Dice, precision, and accuracy indexes were 0.86 ± 0.03, 0.86 ± 0.06, and 0.88 ± 0.02, respectively, thus confirming the reliability of the method in highlighting tumor boundaries. Sensitivity and specificity analysis demonstrated that there is no systematic over- or underestimation of the tumor region. Identification of the tumor boundaries using CEPR resulted in a more accurate and precise definition of GTV compared to that obtained from pretreatment CT. Conclusions: In most proton centers, the current clinical protocol is to align the patient using kV imaging with bony anatomy as a reference. We demonstrated that CEPR can significantly improve tumor visualization, allowing better patient set-up and permitting image guided proton therapy (IGPT)

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗