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DOE OSTI2013

Innovative Gamma Ray Spectrometer Detection Systems for Conducting Scanning Surveys on Challenging Terrain - 13583

The Santa Susana Field Laboratory located near Simi Valley, California was investigated to determine the nature and extent of gamma radiation anomalies. The primary objective was to conduct gamma scanning surveys over 100 percent of the approximately 1,906,000 square meters (471 acre) project site with the most sensitive detection system possible. The site had challenging topography that was not conducive to traditional gamma scanning detection systems. Terrain slope varied from horizontal to 48 degrees and the ground surface ranged from flat, grassy meadows to steep, rocky hillsides. In addition, the site was home to many protected endangered plant and animal species, and archaeologically significant sites that required minimal to no disturbance of the ground surface. Therefore, four innovative and unique gamma ray spectrometer detection systems were designed and constructed to successfully conduct gamma scanning surveys of approximately 1,076,000 square meters (266 acres) of the site. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Integrating Commercial Ready Autonomous Robotic Platforms to Enhance Worker Safety and Efficiency - 20053

This paper will explore the challenges and triumphs of implementing advanced robotic systems and the supporting technology for the waste management industry. Deployment-ready solutions for autonomous robotic platforms are new to the radioactive waste management industry and have been slow to implement across the complex. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground-based autonomous robotic platforms to enhance workforce safety and efficiency. The first objective of the project is to demonstrate safe operation of autonomous instrumented robots, followed by a scaling up of capabilities for the platform to meet any additional requests of internal stakeholders. Autonomous robotics platforms offer a fantastic range of capabilities that will aid in decreasing long-term liability of radioactive waste management industry by enhancing clean-up efforts and worker safety. Over the past five years, deployment-ready solutions for autonomous robotic platforms have come a long way. Autonomous robotic platforms are increasingly available to purchase from many vendors, often for prices under $100,000. Additionally, the vendors who produce these products are efficient in accommodating customized solutions to unique challenges, such as those often found in the radioactive waste management industry. However, the newness of this technology to the radioactive waste management industry and has resulted in a slow implementation across the complex. This paper will explore the challenges and triumphs of implementing advanced robotic systems and supporting technology for the waste management industry. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground based autonomous robotic platforms to enhance workforce safety and efficiency. While the company has utilized robots in many parts of the tank farms mission, this was first attempted in the deployment of an autonomous platform that required no operator with only minor maintenance and created challenges not yet encountered. These challenges included stakeholder buy-in, operational safety, procurement uncertainties, adaption of 'off the shelf products, overcoming the 'Fukushima complexity' perception, security concerns, and the initial criteria down select. Hanford Tank Operations Contractor's management identified the need for a workforce protecting low cost robotics solution in the fall of 2017. The project scope generated was part of the response to the Tank Vapor Assessment Team recommendation to enhance the safety of the workforce by utilizing new technology. Since the projects inception, it has been incorporated into the Tank Stewardship Plan. This project was unique to Hanford Tank Operations Contractor, as the company does not frequently incorporate advanced technology for field deployment quickly. The project was also progressive to the Hanford Tank Operations Contractor as it functions as a partnership project with a local university. The first objective of the project is to demonstrate safe operations of autonomous instrumented robotics, monitoring and collecting data, and the wireless transmission of data to a central computing system. Following the completion of this first objective, the project will scale up capabilities of the platform to meet the requests of additional internal stakeholders throughout the tank farms - including completion of specific in-field tasks. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis of Multi-target Drug Effect Prediction

Graph neural networks (GNNs) have emerged as one of the most effective Machine learning (ML) techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using data sets that contain high dimensional asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects is beyond practicality. Such an implication provides a testbed to address this challenge as multi-target prediction problems, aiming to predict all drug effects at a time. We develop standard and hybrid graph neural networks (GNNs)to perform two separate tasks that are multi-regression for continuous values and multi-label classification for categorical values contained in our data sets. Since this step makes the target data even more sparse and introduces asymmetric label co-occurrence, the learning of multi-label classification models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multi-label classification performances on all the given imbalanced molecular graph data sets. Using the technique, we improve the data imbalance ratio of the drug effects better than before while protecting the data set's integrity. Finally, we evaluate multi-label classification performance using the best-performant hybrid GNN model on all the oversampled data sets obtained from the proposed oversampling technique. These results outperform those of other ML models including GNN models when they are trained on the original data sets or oversampled data sets using MLSMOTE (a well-known oversampling technique) in all evaluation metrics precision, recall, and F1 score by a significant margin.

Bose, Avishek [ORNL]↗

DOE OSTI2018

The additive to background assumption in cancer risk assessment: A reappraisal

The assumption that chemical and radiation induced cancers act in a manner that is additive to background was proposed in the mid-1970s. It was adopted by the U.S. Environmental Protection Agency (EPA) in 1986 and then subsequently by other regulatory agencies worldwide for cancer risk assessment. It ensured that cancer risks at low doses act in a linear fashion. The additive to background process assumes that the mechanism(s) resulting in induced (i.e., treatment related) and spontaneous (i.e., control group) cancers are identical. This assumption could not be properly evaluated due to inadequate mechanistic data when it was proposed in the 1970s. Using the findings of modern molecular toxicology, including oncogene activation/mutation, gene regulation, and molecular pathway analyses, the additive to background assumption was evaluated in the present paper. Based on published studies with 45 carcinogens over 13 diverse mammalian models and for a broad range of tumor types compelling evidence indicates that carcinogen-induced tumors are mediated in general via mechanisms that are not identical to those affecting the occurrence of the same type of spontaneous tumors in appropriate control groups. These findings, which challenge a fundamental assumption of the additive to background concept, have significant implications for cancer risk assessment policy, regulatory agency practices, as well as fundamental concepts of cancer biology.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Single-Dose Vaccination with a Hepatotropic Adeno-associated Virus Efficiently Localizes T Cell Immunity in the Liver with the Potential To Confer Rapid Protection against Hepatitis C Virus

Hepatitis C virus (HCV) is a significant contributor to the global disease burden, and development of an effective vaccine is required to eliminate HCV infections worldwide. CD4 + and CD8 + T cell immunity correlates with viral clearance in primary HCV infection, and intrahepatic CD8 + tissue-resident memory T (T RM ) cells provide lifelong and rapid protection against hepatotropic pathogens. Consequently, we aimed to develop a vaccine to elicit HCV-specific CD4 + and CD8 + T cells, including CD8 + T RM cells, in the liver, given that HCV primarily infects hepatocytes. To achieve this, we vaccinated wild-type BALB/c mice with a highly immunogenic cytolytic DNA vaccine encoding a model HCV (genotype 3a) nonstructural protein (NS5B) and a mutant perforin (pVAX-NS5B-PRF), as well as a recombinant adeno-associated virus (AAV) encoding NS5B (rAAV-NS5B). A novel fluorescent target array (FTA) was used to map immunodominant CD4 + T helper (T H ) cell and cytotoxic CD8 + T cell epitopes of NS5B in vivo , which were subsequently used to design a K d NS5B 451-459 tetramer and analyze NS5B-specific T cell responses in vaccinated mice in vivo . The data showed that intradermal prime/boost vaccination with pVAX-NS5B-PRF was effective in eliciting T H and cytotoxic CD8 + T cell responses and intrahepatic CD8 + T RM cells, but a single intravenous dose of hepatotropic rAAV-NS5B was significantly more effective. As a T-cell-based vaccine against HCV should ideally result in localized T cell responses in the liver, this study describes primary observations in the context of HCV vaccination that can be used to achieve this goal. There are currently at least 71 million individuals with chronic HCV worldwide and almost two million new infections annually. Although the advent of direct-acting antivirals (DAAs) offers highly effective therapy, considerable remaining challenges argue against reliance on DAAs for HCV elimination, including high drug cost, poorly developed health infrastructure, low screening rates, and significant reinfection rates. Accordingly, development of an effective vaccine is crucial to HCV elimination. An HCV vaccine that elicits T cell immunity in the liver will be highly protective for the following reasons: (i) T cell responses against nonstructural proteins of the virus are associated with clearance of primary infection, and (ii) long-lived liver-resident T cells alone can protect against malaria infection of hepatocytes. Thus, in this study we exploit promising vaccination platforms to highlight strategies that can be used to evoke highly functional and long-lived T cell responses in the liver for protection against HCV.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Exploring the Interface of Skin-Layered Titanium Fibers for Electrochemical Water Splitting

Water electrolysis is the key to a decarbonized energy system, as it enables the conversion and storage of renewably generated intermittent electricity in the form of hydrogen. However, reliability challenges arising from titanium-based porous transport layers (PTLs) have hitherto restricted the deployment of next-generation water-splitting devices. Here, it is shown for the first time how PTLs can be adapted so that their interface remains well protected and resistant to corrosion across ˜4000 h under real electrolysis conditions. It is also demonstrated that the malfunctioning of unprotected PTLs is a result triggered by additional fatal degradation mechanisms over the anodic catalyst layer beyond the impacts expected from iridium oxide stability. Now, superior durability and efficiency in water electrolyzers can be achieved over extended periods of operation with less-expensive PTLs with proper protection, which can be explained by the detailed reconstruction of the interface between the different elements, materials, layers, and components presented in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Inferring pesticide toxicity to honey bees from a field‐based feeding study using a colony model and Bayesian inference

Abstract Honey bees are crucial pollinators for agricultural crops but are threatened by a multitude of stressors including exposure to pesticides. Linking our understanding of how pesticides affect individual bees to colony‐level responses is challenging because colonies show emergent properties based on complex internal processes and interactions among individual bees. Agent‐based models that simulate honey bee colony dynamics may be a tool for scaling between individual and colony effects of a pesticide. The U.S. Environmental Protection Agency (USEPA) and U.S. Department of Agriculture (USDA) are developing the VarroaPop + Pesticide model, which simulates the dynamics of honey bee colonies and how they respond to multiple stressors, including weather, Varroa mites, and pesticides. To evaluate this model, we used Approximate Bayesian Computation to fit field data from an empirical study where honey bee colonies were fed the insecticide clothianidin. This allowed us to reproduce colony feeding study data by simulating colony demography and mortality from ingestion of contaminated food. We found that VarroaPop + Pesticide was able to fit general trends in colony population size and structure and reproduce colony declines from increasing clothianidin exposure. The model underestimated adverse effects at low exposure (36 µg/kg), however, and overestimated recovery at the highest exposure level (140 µg/kg), for the adult and pupa endpoints, suggesting that mechanisms besides oral toxicity‐induced mortality may have played a role in colony declines. The VarroaPop + Pesticide model estimates an adult oral LD 50 of 18.9 ng/bee (95% CI 10.1–32.6) based on the simulated feeding study data, which falls just above the 95% confidence intervals of values observed in laboratory toxicology studies on individual bees. Overall, our results demonstrate a novel method for analyzing colony‐level data on pesticide effects on bees and making inferences on pesticide toxicity to individual bees.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance

Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.

3D printing↗

DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗

DOE OSTI2019

A Mono-cuboctahedral Series of Gold Nanoclusters: Photoluminescence Origin, Large Enhancement, Wide Tunability, and Structure–Property Correlation

The origin of the near-infrared photoluminescence (PL) from thiolate-protected gold nanoclusters (Au NCs, <2 nm) has long been controversial, and the exact mechanism for the enhancement of quantum yield (QY) in many works remains elusive. Meanwhile, based upon the sole steady-state PL analysis, it is still a major challenge for researchers to map out a definitive relationship between the atomic structure and the PL property and understand how the Au(0) kernel and Au(I)–S surface contribute to the PL of Au NCs. Herein, we provide a paradigm study to address the above critical issues. By using a correlated series of “mono-cuboctahedral kernel” Au NCs and combined analyses of steady-state, temperature-dependence, femtosecond transient absorption, and Stark spectroscopy measurements, we have explicitly mapped out a kernel-origin mechanism and clearly elucidate the surface–structure effect, which establishes a definitive atomic-level structure–emission relationship. A ~100-fold enhancement of QY is realized via suppression of two effects: (i) the ultrafast kernel relaxation and (ii) the surface vibrations. Furthermore the new insights into the PL origin, QY enhancement, wavelength tunability, and structure–property relationship constitute a major step toward the fundamental understanding and structural-tailoring-based modulation and enhancement of PL from Au NCs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

NEXAFS imaging to characterize the physio-chemical composition of cuticle from African Flower Scarab Eudicella gralli

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99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Converting existing optical detectors into fast x-ray detectors

Here, the very short burn time and small size of burning plasmas created at advanced laser-fusion facilities will require high-spatial-resolution imaging diagnostics with fast time resolution. These instruments will need to function in an environment of extremely large neutron fluxes that will cause conventional diagnostics to fail because of radiation damage and induced background levels. One solution to this challenge is to perform an ultrafast conversion of the x-ray signals into the optical regime before the neutrons are able to reach the detector and then to relay image the signal out of the chamber and into a shielded bunker, protected from the effects of these neutrons. With this goal in mind, the OMEGA laser was used to demonstrate high-temporal-resolution x-ray imaging by using an x-ray snout to image an imploding backlighter capsule onto a semiconductor. The semiconductor was simultaneously probed with the existing velocity interferometry system for any surface reflector (VISAR) diagnostic, which uses an optical streak camera and provided a one-dimensional image of the phase in the semiconductor as a function of time. The phase induced in the semiconductor was linearly proportional to the x-ray emission from the backlighter capsule. This approach would then allow a sacrificial semiconductor to be attached at the end of an optical train with the VISAR and optical streak camera placed in a shielded bunker to operate in a high neutron environment and obtain time-dependent one-dimensional x-ray images or time-dependent x-ray spectra from a burning plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Nonreciprocal Dissipation Engineering via Strong Coupling with a Continuum of Modes

Optical nonreciprocity plays a key role in almost every optical system, directing light flow and protecting optical components from backscattered light. Controllable forms of on-chip nonreciprocity are needed for the robust operation of increasingly sophisticated photonic integrated circuits (PICs) in the context of classical and quantum computation, networking, communications, and sensing. However, it has been challenging to achieve wideband, low-loss optical nonreciprocity on-chip. In this paper, we demonstrate strong coupling and Rabi-like energy exchange between photonic bands, possessing a continuum of modes, to unlock nonreciprocity and frequency translation over wide optical bandwidths in silicon. Using a traveling-wave phonon field to drive indirect interband photonic transitions, we demonstrate band hybridization that enables an intriguing form of nonreciprocal dissipation engineering. Using the converted mode to create a nonreciprocal dissipation channel, we demonstrate a frequency-neutral, low-loss (less than 1 dB) isolator with high nonreciprocal contrast (more than 14 dB) and broad operating bandwidth (more than 59 GHz). Additionally, through the implementation of complete interband conversion, we demonstrate a high extinction (more than 55 dB) optical frequency translation operation with near-unity efficiency.

Zhou, Yishu↗

DOE OSTI2015

MO-B-BRB-00: Optimizing the Treatment Planning Process

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-EF-BRD-01: Past, Present and Future: MRI-Guided Radiotherapy From 2005 to 2025

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-EF-BRD-02: Battling Maxwell’s Equations: Physics Challenges and Solutions for Hybrid MRI Systems

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-EF-BRD-03: I Want It Now!: Advances in MRI Acquisition, Reconstruction and the Use of Priors to Enable Fast Anatomic and Physiologic Imaging to Inform Guidance and Adaptation Decisions

60 APPLIED LIFE SCIENCES↗