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DOE OSTI2016

An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy

Purpose: For the first time, MRI-guided radiation therapy systems can acquire cine images to dynamically monitor in-treatment internal organ motion. However, the complex head and neck (H&N) structures and low-contrast/resolution of on-board cine MRI images make automatic motion tracking a very challenging task. In this study, the authors proposed an integrated model-driven method to automatically track the in-treatment motion of the H&N upper airway, a complex and highly deformable region wherein internal motion often occurs in an either voluntary or involuntary manner, from cine MRI images for the analysis of H&N motion patterns. Methods: Considering the complex H&N structures and ensuring automatic and robust upper airway motion tracking, the authors firstly built a set of linked statistical shapes (including face, face-jaw, and face-jaw-palate) using principal component analysis from clinically approved contours delineated on a set of training data. The linked statistical shapes integrate explicit landmarks and implicit shape representation. Then, a hierarchical model-fitting algorithm was developed to align the linked shapes on the first image frame of a to-be-tracked cine sequence and to localize the upper airway region. Finally, a multifeature level set contour propagation scheme was performed to identify the upper airway shape change, frame-by-frame, on the entire image sequence. The multifeature fitting energy, including the information of intensity variations, edge saliency, curve geometry, and temporal shape continuity, was minimized to capture the details of moving airway boundaries. Sagittal cine MR image sequences acquired from three H&N cancer patients were utilized to demonstrate the performance of the proposed motion tracking method. Results: The tracking accuracy was validated by comparing the results to the average of two manual delineations in 50 randomly selected cine image frames from each patient. The resulting average dice similarity coefficient (93.28% ± 1.46%) and margin error (0.49 ± 0.12 mm) showed good agreement between the automatic and manual results. The comparison with three other deformable model-based segmentation methods illustrated the superior shape tracking performance of the proposed method. Large interpatient variations of swallowing frequency, swallowing duration, and upper airway cross-sectional area were observed from the testing cine image sequences. Conclusions: The proposed motion tracking method can provide accurate upper airway motion tracking results, and enable automatic and quantitative identification and analysis of in-treatment H&N upper airway motion. By integrating explicit and implicit linked-shape representations within a hierarchical model-fitting process, the proposed tracking method can process complex H&N structures and low-contrast/resolution cine MRI images. Future research will focus on the improvement of method reliability, patient motion pattern analysis for providing more information on patient-specific prediction of structure displacements, and motion effects on dosimetry for better H&N motion management in radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

From stochastic processes to numerical methods: A new scheme for solving reaction subdiffusion fractional partial differential equations

We have introduced a new explicit numerical method, based on a discrete stochastic process, for solving a class of fractional partial differential equations that model reaction subdiffusion. The scheme is derived from the master equations for the evolution of the probability density of a sum of discrete time random walks. We show that the diffusion limit of the master equations recovers the fractional partial differential equation of interest. This limiting procedure guarantees the consistency of the numerical scheme. The positivity of the solution and stability results are simply obtained, provided that the underlying process is well posed. We also show that the method can be applied to standard reaction–diffusion equations. This work highlights the broader applicability of using discrete stochastic processes to provide numerical schemes for partial differential equations, including fractional partial differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Improving Solar and Solar+Storage Screening Techniques to Reduce Utility Interconnection Time and Costs (Final Technical Report)

Residential PV installations have increased rapidly over the last decade, and the increased application volume has caused permitting delays and lower overall adoption rates. In this project, we developed and evaluated whether data-driven secondary modeling and screening techniques can help utilities assess customer applications more accurately than traditional screening shortcuts. Secondary topologies are predicted using decision trees and commonly available information, such as service transformer, customer, and street locations. Conductors were predicted using a logistic regression method based on real world object (RWO) types, service transformer ratings, conductor length, and distance to transformer. After developing the combined primary and secondary distribution network model, hosting capacity results were used to train a random forest model to predict the pass/fail likelihood of a customer application. Powerflow based models with predicted secondaries and data-driven methods both increased the screening success rate, relative to common utility heuristics, by as much as 55 percentage points. Data-driven screening techniques were described by one utility as a "right-sized" approach for residential customers given the low-risk of small errors and the high-cost of accurate modeling.

14 SOLAR ENERGY↗

DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

Our ability to study and valorize the lignin fraction of biomass is hampered by the fundamental and still unmet challenge of precisely quantifying native lignin's structural features. Here, we developed a rapid elevated-temperature 1 H– 13 C Heteronuclear Single-Quantum Coherence Zero (HSQC 0 ) NMR method that enables this precise quantification of native lignin structural characteristics even with whole plant cell wall (WPCW) NMR spectroscopy, overcoming fast spin relaxation in the gel phase. We also formulated a Gaussian fitting algorithm to perform automatic and reliable spectral integration. By combining HSQC 0 measurements with yield measurements following depolymerisation, we can confirm the combinatorial nature of radical coupling reactions during biosynthesis leading to a random sequential organization of linkages within a largely linear lignin chain. Such analyses illustrate how this analytical method can greatly facilitate the study of native lignin structure, which can then be used for fundamental studies or to understand lignin depolymerization methods like reductive catalytic fractionation or aldehyde-assisted fractionation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

09 BIOMASS FUELS↗

DOE OSTI2014

Likelihood-informed dimension reduction for nonlinear inverse problems

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively low-dimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the 'likelihood-informed subspace' (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao–Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.

42 ENGINEERING↗

DOE OSTI2023

Fast truncated SVD of sparse and dense matrices on graphics processors

We investigate the solution of low-rank matrix approximation problems using the truncated singular value decomposition (SVD). For this purpose, we develop and optimize graphics processing unit (GPU) implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be assembled using numerical kernels from existing high-performance linear algebra libraries. Furthermore, the experiments with several sparse matrices arising in representative real-world applications and synthetic dense test matrices reveal a performance advantage of the block Lanczos algorithm when targeting the same approximation accuracy.

Computer Science↗

DOE OSTI2017

Filters for Improvement of Multiscale Data from Atomistic Simulations

Multiscale computational models strive to produce accurate and efficient numerical simulations of systems involving interactions across multiple spatial and temporal scales that typically differ by several orders of magnitude. Some such models utilize a hybrid continuum-atomistic approach combining continuum approximations with first-principles-based atomistic models to capture multiscale behavior. By following the heterogeneous multiscale method framework for developing multiscale computational models, unknown continuum scale data can be computed from an atomistic model. Concurrently coupling the two models requires performing numerous atomistic simulations which can dominate the computational cost of the method. Furthermore, when the resulting continuum data is noisy due to sampling error, stochasticity in the model, or randomness in the initial conditions, filtering can result in significant accuracy gains in the computed multiscale data without increasing the size or duration of the atomistic simulations. In this work, we demonstrate the effectiveness of spectral filtering for increasing the accuracy of noisy multiscale data obtained from atomistic simulations. Moreover, we present a robust and automatic method for closely approximating the optimum level of filtering in the case of additive white noise. By improving the accuracy of this filtered simulation data, it leads to a dramatic computational savings by allowing for shorter and smaller atomistic simulations to achieve the same desired multiscale simulation precision.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Cluster Variation Method Analysis of Correlations and Entropy in BCC Solid Solutions

Solid solutions occur when multiple chemical species share sites of a common crystal lattice. Although the single site occupation is random, chemical interaction preferences bias the occupation probabilities of neighboring sites, and this bias reduced the entropy of mixing below its ideal value. Sufficiently strong bias leads to symmetry-breaking phase transitions. Here, we apply the cluster variation method to explore solid solutions on body centered cubic lattices in the context of two specific compounds that exhibit opposite ordering trends. Employing density functional theory to model the energetics, we show that CuZn exhibits an order-disorder transition to the CsCl prototype structure, while AlLi instead takes the NaTl prototype structure, and we evaluate their temperature-dependent order parameters, correlations and entropies.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Quantitative Assessment of Breast Cosmetic Outcome After Whole-Breast Irradiation

Purpose: To measure, by quantitative analysis of digital photographs, breast cosmetic outcome within the setting of a randomized trial of conventionally fractionated (CF) and hypofractionated (HF) whole-breast irradiation (WBI), to identify how quantitative cosmesis metrics were associated with patient- and physician-reported cosmesis and whether they differed by treatment arm. Methods and Materials: From 2011 to 2014, 287 women aged ≥40 with ductal carcinoma in situ or early invasive breast cancer were randomized to HF-WBI (42.56 Gy/16 fractions [fx] + 10-12.5 Gy/4-5 fx boost) or CF-WBI (50 Gy/25 fx + 10-14 Gy/5-7 fx). At 1 year after treatment we collected digital photographs, patient-reported cosmesis using the Breast Cancer Treatment and Outcomes Scale, and physician-reported cosmesis using the Radiation Therapy Oncology Group scale. Six quantitative measures of breast symmetry, labeled M1-M6, were calculated from anteroposterior digital photographs. For each measure, values closer to 1 imply greater symmetry, and values closer to 0 imply greater asymmetry. Associations between M1-M6 and patient- and physician-reported cosmesis and treatment arm were evaluated using the Kruskal-Wallis test. Results: Among 245 evaluable patients, patient-reported cosmesis was strongly associated with M1 (vertical symmetry measure) (P<.01). Physician-reported cosmesis was similarly correlated with M1 (P<.01) and also with M2 (vertical symmetry, P=.01) and M4 (horizontal symmetry, P=.03). At 1 year after treatment, HF-WBI resulted in better values of M2 (P=.02) and M3 (P<.01) than CF-WBI; treatment arm was not significantly associated with M1, M4, M5, or M6 (P≥.12). Conclusions: Quantitative assessment of breast photographs reveals similar to improved cosmetic outcome with HF-WBI compared with CF-WBI 1 year after treatment. Assessing cosmetic outcome using these measures could be useful for future comparative effectiveness studies and outcome reporting.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗

DOE OSTI2021

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

A Phase 3 Trial of 2 Years of Androgen Suppression and Radiation Therapy With or Without Adjuvant Chemotherapy for High-Risk Prostate Cancer: Final Results of Radiation Therapy Oncology Group Phase 3 Randomized Trial NRG Oncology RTOG 9902

Purpose: Long-term (LT) androgen suppression (AS) with radiation therapy (RT) is a standard treatment of high-risk, localized prostate cancer (PCa). Radiation Therapy Oncology Group 9902 was a randomized trial testing the hypothesis that adjuvant combination chemotherapy (CT) with paclitaxel, estramustine, and oral etoposide plus LT AS plus RT would improve overall survival (OS). Methods and Materials: Patients with high-risk PCa (prostate-specific antigen 20-100 ng/mL and Gleason score [GS] ≥7 or clinical stage ≥T2 and GS ≥8) were randomized to RT and AS (AS + RT) alone or with adjuvant CT (AS + RT + CT). CT was given as four 21-day cycles, delivered beginning 28 days after 70.2 Gy of RT. AS was given as luteinizing hormone-releasing hormone for 24 months, beginning 2 months before RT plus an oral antiandrogen for 4 months before and during RT. The study was designed based on a 6% improvement in OS from 79% to 85% at 5 years, with 90% power and a 2-sided alpha of 0.05. Results: A total of 397 patients (380 eligible) were randomized. The patients had high-risk PCa, 68% with GS 8 to 10 and 34% T3 to T4 tumors, and median prostate-specific antigen of 22.6 ng/mL. The median follow-up period was 9.2 years. The trial closed early because of excess thromboembolic toxicity in the CT arm. The 10-year results for all randomized patients revealed no significant difference between the AS + RT and AS + RT + CT arms in OS (65% vs 63%; P=.81), biochemical failure (58% vs 54%; P=.82), local progression (11% vs 7%; P=.09), distant metastases (16% vs 14%; P=.42), or disease-free survival (22% vs 26%; P=.61). Conclusions: NRG Oncology RTOG 9902 showed no significant differences in OS, biochemical failure, local progression, distant metastases, or disease-free survival with the addition of adjuvant CT to LT AS + RT. The trial results provide valuable data regarding the natural history of high-risk PCa treated with LT AS + RT and have implications for the feasibility of clinical trial accrual and tolerability using CT for PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Slate – A new record for crystal preferred orientation

This report describes microstructures and preferred orientation of a Devonian slate from the Belgian Ardennes (La Fortelle quarry). The sample, composed largely of muscovite, chlorite, and quartz, displays extraordinary texture strength of muscovite, exceeding 100 multiples of random distribution (m.r.d.) for (001) pole figures and thus setting a new record for preferred orientation in rocks. Quantitative texture analysis was performed with the Rietveld method on synchrotron X-ray diffraction images. Interestingly, quartz also displays strong shape preferred orientation (SPO) as revealed by X-ray microtomography and SEM imaging but largely random crystallographic preferred orientation (CPO). In conclusion, microstructural evidence suggests that this fine-grained rock formed by recrystallization of shale under stress, with growth of phyllosilicates and dissolution and growth of quartz.

58 GEOSCIENCES↗

DOE OSTI2024

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Non-equilibrium Green's functions method: Non-trivial and disordered leads

The non-equilibrium Green's function algorithm requires contact self-energies to model charge injection and extraction. All existing approaches assume infinitely periodic leads attached to a possibly quite complex device. This contradicts today's realistic devices in which contacts are spatially inhomogeneous, chemically disordered, and impacting the overall device characteristics. This work extends the complex absorbing potentials method for arbitrary, ideal, or non-ideal leads in atomistic tight binding representation. The algorithm is demonstrated on a Si nanowire with periodic leads, a graphene nanoribbon with trumpet shape leads, and devices with leads of randomly alloyed Si{sub 0.5}Ge{sub 0.5}. It is found that alloy randomness in the leads can reduce the predicted ON-state current of Si{sub 0.5}Ge{sub 0.5} transistors by 45% compared to conventional lead methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

Methods for obtaining true particle size distributions from cross section measurements

Sectioning methods are frequently used to measure grain sizes in materials. These methods do not provide accurate grain sizes for two reasons. First, the sizes of features observed on random sections are always smaller than the true sizes of solid spherical shaped objects, as noted by Wicksell [1]. This is the case because the section very rarely passes through the center of solid spherical shaped objects randomly dispersed throughout a material. The sizes of features observed on random sections are inversely related to the distance of the center of the solid object from the section [1]. Second, on a plane section through the solid material, larger sized features are more frequently observed than smaller ones due to the larger probability for a section to come into contact with the larger sized portion of the spheres than the smaller sized portion. As a result, it is necessary to find a method that takes into account these reasons for inaccurate particle size measurements, while providing a correction factor for accurately determining true particle size measurements. I present a method for deducing true grain size distributions from those determined from specimen cross sections, either by measurement of equivalent grain diameters or linear intercepts.

36 MATERIALS SCIENCE↗