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DOE OSTI2016

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

Purpose: The pretreatment physics plan review is a standard tool for ensuring treatment quality. Studies have shown that the majority of errors in radiation oncology originate in treatment planning, which underscores the importance of the pretreatment physics plan review. This quality assurance measure is fundamentally important and central to the safety of patients and the quality of care that they receive. However, little is known about its effectiveness. The purpose of this study was to analyze reported incidents to quantify the effectiveness of the pretreatment physics plan review with the goal of improving it. Methods: This study analyzed 522 potentially severe or critical near-miss events within an institutional incident learning system collected over a three-year period. Of these 522 events, 356 originated at a workflow point that was prior to the pretreatment physics plan review. The remaining 166 events originated after the pretreatment physics plan review and were not considered in the study. The applicable 356 events were classified into one of the three categories: (1) events detected by the pretreatment physics plan review, (2) events not detected but “potentially detectable” by the physics review, and (3) events “not detectable” by the physics review. Potentially detectable events were further classified by which specific checks performed during the pretreatment physics plan review detected or could have detected the event. For these events, the associated specific check was also evaluated as to the possibility of automating that check given current data structures. For comparison, a similar analysis was carried out on 81 events from the international SAFRON radiation oncology incident learning system. Results: Of the 356 applicable events from the institutional database, 180/356 (51%) were detected or could have been detected by the pretreatment physics plan review. Of these events, 125 actually passed through the physics review; however, only 38% (47/125) were actually detected at the review. Of the 81 events from the SAFRON database, 66/81 (81%) were potentially detectable by the pretreatment physics plan review. From the institutional database, three specific physics checks were particularly effective at detecting events (combined effectiveness of 38%): verifying the isocenter (39/180), verifying DRRs (17/180), and verifying that the plan matched the prescription (12/180). The most effective checks from the SAFRON database were verifying that the plan matched the prescription (13/66) and verifying the field parameters in the record and verify system against those in the plan (23/66). Software-based plan checking systems, if available, would have potential effectiveness of 29% and 64% at detecting events from the institutional and SAFRON databases, respectively. Conclusions: Pretreatment physics plan review is a key safety measure and can detect a high percentage of errors. However, the majority of errors that potentially could have been detected were not detected in this study, indicating the need to improve the pretreatment physics plan review performance. Suggestions for improvement include the automation of specific physics checks performed during the pretreatment physics plan review and the standardization of the review process.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Modeling of time-lapse seismic monitoring using CO 2 leakage simulations for a model CO 2 storage site with realistic geology: Application in assessment of early leak-detection capabilities

Time-lapse surface seismic surveys have been widely used at carbon sequestration sites for site characterization, monitoring subsurface CO 2 plume migration, and detecting potential CO 2 leakage from a storage reservoir. Monitoring in the first permeable unit directly above the primary seal is important for early detection of CO 2 leakage. Forward modeling of time-lapse seismic data can be used to assess the utility of the seismic method for CO 2 leakage detection. We develop a workflow for forward modeling of time-lapse seismic data, including constructing seismic velocity models using flow simulation outputs, modeling of pre-stack and post-stack synthetic seismic data following seismic data processing sequence and analysis of processed synthetic time-lapse seismic data. We apply the forward modeling and analysis workflow to assessing the detectability and the earliest detection time of seismic monitoring using the hypothetical CO 2 leakage scenarios for a model geologic storage site with realistic geology. We then derive the detection thresholds using the simulated normalized root-mean-square (NRMS) differences for the no-leakage case at a range of signal-to-noise ratios, representing the background noise levels in seismic data. We then compare NRMS differences triggered by the CO 2 leakage to the detection thresholds at each time step to quantify the detectability and the earliest detection time of seismic monitoring. We analyze the effects of the acquisition parameters and elastic parameters on the produced synthetic seismic data and earliest detection time. Our modeling results indicate that high signal-to-noise ratio is needed to detect the CO 2 leakage at the model site. Minimizing the background noise in seismic data is crucial for improving the detectability of the seismic method. Increasing the shot density or increasing the dominant frequency of the source wavelet is likely to increase the possibility of the leakage detection and reduce the time needed for the detection. The elastic parameters used in the rock physics modeling have significant effects on the resultant seismic velocity models and synthetic seismic data, highlighting their critical roles in understanding and interpreting time-lapse seismic reflection data associated with CO 2 monitoring, verification and accounting activities.

58 GEOSCIENCES↗

DOE OSTI2015

SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

Detection of supernovae (SNe) and, more generally, of transient events in large surveys can provide numerous false detections. In the case of a deferred processing of survey images, this implies reconstructing complete light curves for all detections, requiring sizable processing time and resources. Optimizing the detection of transient events is thus an important issue for both present and future surveys. We present here the optimization done in the SuperNova Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this analysis, detections are derived from stacks of subtracted images with one stack per lunation. The 3-year analysis provided 300,000 detections dominated by signals of bright objects that were not perfectly subtracted. Allowing these artifacts to be detected leads not only to a waste of resources but also to possible signal coordinate contamination. We developed a subtracted image stack treatment to reduce the number of non SN-like events using morphological component analysis. This technique exploits the morphological diversity of objects to be detected to extract the signal of interest. At the level of our subtraction stacks, SN-like events are rather circular objects while most spurious detections exhibit different shapes. A two-step procedure was necessary to have a proper evaluation of the noise in the subtracted image stacks and thus a reliable signal extraction. We also set up a new detection strategy to obtain coordinates with good resolution for the extracted signal. SNIa Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2015

Improved Detection System Description and New Method for Accurate Calibration of Micro-Channel Plate Based Instruments and Its Use in the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

The Fast Plasma Investigation (FPI) on NASAs Magnetospheric MultiScale (MMS) mission employs 16 Dual Electron Spectrometers (DESs) and 16 Dual Ion Spectrometers (DISs) with 4 of each type on each of 4 spacecraft to enable fast (30 ms for electrons; 150 ms for ions) and spatially differentiated measurements of the full 3D particle velocity distributions. This approach presents a new and challenging aspect to the calibration and operation of these instruments on ground and in flight. The response uniformity, the reliability of their calibration and the approach to handling any temporal evolution of these calibrated characteristics all assume enhanced importance in this application, where we attempt to understand the meaning of particle distributions within the ion and electron diffusion regions of magnetically reconnecting plasmas. Traditionally, the micro-channel plate (MCP) based detection systems for electrostatic particle spectrometers have been calibrated using the plateau curve technique. In this, a fixed detection threshold is set. The detection system count rate is then measured as a function of MCP voltage to determine the MCP voltage that ensures the count rate has reached a constant value independent of further variation in the MCP voltage. This is achieved when most of the MCP pulse height distribution (PHD) is located at higher values (larger pulses) than the detection system discrimination threshold. This method is adequate in single-channel detection systems and in multi-channel detection systems with very low crosstalk between channels. However, in dense multi-channel systems, it can be inadequate. Furthermore, it fails to fully describe the behavior of the detection system and individually characterize each of its fundamental parameters. To improve this situation, we have developed a detailed phenomenological description of the detection system, its behavior and its signal, crosstalk and noise sources. Based on this, we have devised a new detection system calibration method that enables accurate and repeatable measurement and calibration of MCP gain, MCP efficiency, signal loss due to variation in gain and efficiency, crosstalk from effects both above and below the MCP, noise margin, and stability margin in one single measurement. More precise calibration is highly desirable as the instruments will produce higher quality raw data that will require less post-acquisition data correction using results from in-flight pitch angle distribution measurements and ground calibration measurements. The detection system description and the fundamental concepts of this new calibration method, named threshold scan, will be presented. It will be shown how to derive all the individual detection system parameters and how to choose the optimum detection system operating point. This new method has been successfully applied to achieve a highly accurate calibration of the DESs and DISs of the MMS mission. The practical application of the method will be presented together with the achieved calibration results and their significance. Finally, it will be shown that, with further detailed modeling, this method can be extended for use in flight to achieve and maintain a highly accurate detection system calibration across a large number of instruments during the mission.

detection system↗

NASA NTRS2022

Large Interferometer for Exoplanets (Life) I. Improved Exoplanet Detection Yield Estimates for A Large Mid-Infrared Space-Interferometer Mission

Context. One of the long-term goals of exoplanet science is the atmospheric characterization of dozens of small exoplanets in order to understand their diversity and search for habitable worlds and potential biosignatures. Achieving this goal requires a space mission of sufficient scale that can spatially separate the signals from exoplanets and their host stars and thus directly scrutinize the exoplanets and their atmospheres. Aims. We seek to quantify the exoplanet detection performance of a space-based mid-infrared (MIR) nulling interferometer that measures the thermal emission of exoplanets. We study the impact of various parameters and compare the performance with that of large single-aperture mission concepts detecting exoplanets in reflected light. Methods. We have developed an instrument simulator that considers all major astrophysical noise sources and coupled it with Monte Carlo of a synthetic exoplanet population around main-sequence stars within 20 pc. This allows us to quantify the number (and types) of exoplanets that our mission concept could detect over a certain time period. Two different scenarios to distribute the observing time among the stellar targets are discussed and different apertures sizes and wavelength ranges are considered. Results. An interferometer consisting of four 2 m apertures working in the 4–18.5 μ.m wavelength range with a total instrument throughput of 5% could detect up to ≈550 exoplanets with radii between 0.5 and 6 R ⊕ with an integrated S/N ≥ 7. At least ≈160 of the detected exoplanets have radii ≤1.5 R ⊕ . Depending on the observing scenario, ≈25–45 rocky exoplanets (objects with radii between 0.5 and 1.5 R ⊕ ) orbiting within the empirical habitable zone (eHZ) of their host stars are among the detections. With four 3.5 m apertures, the total number of detections can increase to up to ≈770, including ≈60–80 rocky eHZ planets. With four times 1 m apertures, the maximum detection yield is ≈315 exoplanets, including ≤20 rocky eHZ planets. The vast majority of small, temperate exoplanets are detected around M dwarfs. The impact of changing the wavelength range to 3–20 μm or 6–17 μm on the detection yield is negligible. Conclusions. A large space-based MIR nulling interferometer will be able to directly detect hundreds of small, nearby exoplanets, tens of which would be habitable world candidates. In terms of predicted detection yield, such a mission can compete with large single-aperture reflected light missions. Given that we assumed only 2.5 years for the search phase, a significant sub-set of the detected exoplanets can be followed-up in a second mission phase to obtain high SNR thermal emission spectra, leveraging the superior diagnostic power of the MIR wavelength regime compared to shorter wavelengths.

planets and satellites: terrestrial planets↗

DOE OSTI2014

Network Algorithms for Detection of Radiation Sources

In support of national defense, Domestic Nuclear Detection Office’s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) a notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2”*2” NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source strength estimate, typically specified as a multiplier of the background radiation level. A judicious selection of this source multiplier is essential to achieve optimal detection probability at a specified false alarm rate. Typically, this threshold is chosen from the Receiver Operating Characteristic (ROC) by varying the source multiplier estimate. ROC is expected to have a monotonically increasing profile between the detection probability and false alarm rate. We derived ROCs for multiple indoor tests using KMB datasets, which revealed an unexpected loop shape: as the multiplier increases, detection probability and false alarm rate both increase until a limit, and then both contract. Consequently, two detection probabilities correspond to the same false alarm rate, and the higher is achieved at a lower multiplier, which is the desired operating point. Using the Chebyshev’s inequality we analytically confirm this shape. Then, we present two improved network-SPRT methods by (a) using the threshold off-set as a weighting factor for the binary decisions from individual detectors in a weighted majority voting fusion rule, and (b) applying a composite SPRT derived using measurements from all counters.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2014

Quantitative Assessment of Detection Frequency for the INL Ambient Air Monitoring Network

A quantitative assessment of the Idaho National Laboratory (INL) air monitoring network was performed using frequency of detection as the performance metric. The INL air monitoring network consists of 37 low-volume air samplers in 31 different locations. Twenty of the samplers are located on INL (onsite) and 17 are located off INL (offsite). Detection frequencies were calculated using both BEA and ESER laboratory minimum detectable activity (MDA) levels. The CALPUFF Lagrangian puff dispersion model, coupled with 1 year of meteorological data, was used to calculate time-integrated concentrations at sampler locations for a 1-hour release of unit activity (1 Ci) for every hour of the year. The unit-activity time-integrated concentration (TICu) values were calculated at all samplers for releases from eight INL facilities. The TICu values were then scaled and integrated for a given release quantity and release duration. All facilities modeled a ground-level release emanating either from the center of the facility or at a point where significant emissions are possible. In addition to ground-level releases, three existing stacks at the Advanced Test Reactor Complex, Idaho Nuclear Technology and Engineering Center, and Material and Fuels Complex were also modeled. Meteorological data from the 35 stations comprising the INL Mesonet network, data from the Idaho Falls Regional airport, upper air data from the Boise airport, and three-dimensional gridded data from the weather research forecasting model were used for modeling. Three representative radionuclides identified as key radionuclides in INL’s annual National Emission Standards for Hazardous Air Pollutants evaluations were considered for the frequency of detection analysis: Cs-137 (beta-gamma emitter), Pu-239 (alpha emitter), and Sr-90 (beta emitter). Source-specific release quantities were calculated for each radionuclide, such that the maximum inhalation dose at any publicly accessible sampler or the National Emission Standards for Hazardous Air Pollutants maximum exposed individual location (i.e., Frenchman’s Cabin) was no more than 0.1 mrem yr–1 (i.e., 1% of the 10 mrem yr–1 standard). Detection frequencies were calculated separately for the onsite and offsite monitoring network. As expected, detection frequencies were generally less for the offsite sampling network compared to the onsite network. Overall, the monitoring network is very effective at detecting the potential releases of Cs-137 or Sr-90 from all sources/facilities using either the ESER or BEA MDAs. The network was less effective at detecting releases of Pu-239. Maximum detection frequencies for Pu-239 using ESER MDAs ranged from 27.4 to 100% for onsite samplers and 3 to 80% for offsite samplers. Using BEA MDAs, the maximum detection frequencies for Pu-239 ranged from 2.1 to 100% for onsite samplers and 0 to 5.9% for offsite samplers. The only release that was not detected by any of the samplers under any conditions was a release of Pu-239 from the Idaho Nuclear Technology and Engineering Center main stack (CPP-708). The methodology described in this report could be used to improve sampler placement and detection frequency, provided clear performance objectives are defined.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Training courses on neutron detection systems on the ISIS research reactor: on-site and through internet training

Today, ISIS research reactor is an essential tool for Education and Training programs organized by the National Institute for Nuclear Science and Technology (INSTN) from CEA. In the field of nuclear instrumentation, the INSTN offers both, theoretical courses and training courses on the use of neutron detection systems taking advantage of the ISIS research reactor for the supply of a wide range of neutron fluxes. This paper describes the content of the training carried out on the use of neutron detectors and detection systems, on-site or remote. The ISIS reactor is a 700 kW open core pool type reactor. The facility is very flexible since neutron detectors can be inserted into the core or its vicinity, and be used at different levels of power according to the needs of the course. Neutron fluxes, typically ranging from 1 to 10{sup 12} n/cm{sup 2}.s, can be obtained for the characterisation of the neutron detectors and detection systems. For the monitoring of the neutron density at low level of power, the Instrumentation and Control (I and C) system of the reactor is equipped with two detection systems, named BN1 and BN2. Each way contains a fission chamber, type CFUL01, connected to an electronic system type SIREX.The system works in pulse mode and exhibits two outputs: the counting rate and the doubling time. For the high level of power, the I and C is equipped with two detection systems HN1 and HN2.Each way contain a boron ionization chamber (type CC52) connected to an electronics system type SIREX. The system works in current mode and has two outputs: the current and the doubling time. For each mode, the trainees can observe and measure the signal at the different stages of the electronic system, with an oscilloscope. They can understand the role of each component of the detection system: detector, cable and each electronic block. The limitation of the detection modes and their operating range can be established from the measured signal. The trainees can also modify the settings of the electronic system, such as the high voltage and the discrimination level in order to obtain all the characteristic curves of the detectors. These curves are used to define the right setting of the electronic system and to discuss the expected degradation of the detector signal resulting from the detector damage under the integrated neutron and gamma fluxes. Moreover, in addition to the study of the neutron detection systems itself, the integration of the measurements made by these detection systems in the logic of the safety system of the nuclear reactor is also addressed. Providing the trainees with an extensive overview of each part of the neutron monitoring instrumentation apply to a nuclear reactor, hands-on measurements on the ISIS reactor play a major role in ensuring a practical and comprehensive understanding of the neutron detection system and their integration in the safety system of nuclear reactors. It also gives a solid background for the follow up and the development of the neutron detection systems. In addition to on-reactor training, Internet Reactor Laboratory capability has been implemented on the ISIS reactor in 2014. For the Internet Reactor Laboratory an extensive video conference system has been implemented on ISIS reactor. The system includes 4 cameras and the transmission of the video signal given by the supervision system of the reactor which records and processes the data of the reactor. According to the pedagogic needs during the training courses, the lecturer on the ISIS reactor chooses to broadcast the relevant information at each stage of the course. For example, graph showing the histogram of the counting and current as a function of the time, or the electrical signal observed on the oscilloscope, can be broadcasted trough internet. By interacting through the video conference, the remote classroom is able to ask for changes in the reactor power or settings of the detection systems. They can also ask for the broadcast of some particular information. At the guest institution, the information is displayed in two parts or screens, as shown in the Figure 3. Concerning the interaction with - and the feedback from - the remote classroom, the camera of the video system in the remote classroom is used to ensure the contact between the trainees and the lecturer and reactor operators. Thus, the Internet Reactor Laboratory is complementary to the on reactor training courses. It allows distant learning, reducing the overall cost of the course when this is necessary. It can efficiently be used for the development of the human resources needed by the nuclear industry and the nuclear programs in countries without research reactors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Hydrogen Detection Strategies to Support H2@SCALE - The NREL Sensor Laboratory

Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.

ENERGY STORAGE,HYDROGEN↗

DOE OSTI2015

Spectral anomaly methods for aerial detection using KUT nuisance rejection

Aerial detection combines all the challenges of standoff detection with all the difficulties of mobile survey, including large and varying standoff distances and abrupt and significant changes in background. Critical issues for aerial detection include land-water interfaces, rapidly changing KUT backgrounds, and varying down scatter with altitude changes. To address these issues a spectral anomaly method has been adapted to aerial detection. The NSCRAD (Nuisance-Rejection Spectral Comparison Ratio) algorithm was initially developed at PNNL for medium resolution detectors. The algorithm has been used for a range of ground-based detection applications, including ground-based detection, portal monitoring, and OSI applications. The method used in NSCRAD includes both optimized energy windows for detection of threat sources and a nuisance rejection capability. Additionally, the spectral anomaly method was optimized in this work for aerial detection systems to increase the probability of detection of threat sources in aerial search while using the nuisance rejection capability to minimize false alarms from rapidly changing aerial background. The adaptation of NSCRAD for aerial detection includes re-optimization of energy windows, adapting KUT nuisance rejection for aerial backgrounds, development of new methods for background tracking and adjustment of tracking and detection thresholds.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Quantifying the Potential of Argon Detection Capabilities for Nuclear Explosion Monitoring

Abstract Current noble gas detection systems for nuclear explosion monitoring are based on the detection of four radioxenon isotopes—Xe-131m, -133, -133m and -135. The data provided by radioxenon detection could be enhanced by other radionuclide signatures such as Ar-37. Activation of Ca-40 in rock by neutrons produces Ar-37, and monitoring for this additional nuclide could help distinguish detections of nuclear explosions from background sources, such as medical isotope production. This work studies the capabilities of a hypothetical argon detection network. A 10 kt explosion was modeled using MCNP and SCALE to determine the inventory of Ar-37 created in a representative granite rock layer, assuming either 0.1, 1 or 10% of the total inventory was released. The Ar-37 inventory was combined with atmospheric transport data from HYSPLIT compiled in a previous study, along with the detection limits of standard Ar-37 detection systems, to determine how many hypothetical monitoring stations would detect Ar-37 from an explosion. This method was repeated for 365 HYSPLIT data sets to create a year’s worth of hypothetical explosions, releases, and detections. The study quantified the average number of detections per release, the number of stations detecting Ar-37, and the possibility of detecting Ar-37 in coincidence with xenon.

37Ar↗

DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗

DOE OSTI2015

SU-E-I-10: Investigation On Detectability of a Small Target for Different Slice Direction of a Volumetric Cone Beam CT Image

Purpose: To investigate the detectability of a small target for different slice direction of a volumetric cone beam CT image and its impact on dose reduction. Methods: Analytic projection data of a sphere object (1 mm diameter, 0.2/cm attenuation coefficient) were generated and reconstructed by FDK algorithm. In this work, we compared the detectability of the small target from four different backprojection Methods: hanning weighted ramp filter with linear interpolation (RECON 1), hanning weighted ramp filter with Fourier interpolation (RECON2), ramp filter with linear interpolation (RECON 3), and ramp filter with Fourier interpolation (RECON4), respectively. For noise simulation, 200 photons per measurement were used, and the noise only data were reconstructed using FDK algorithm. For each reconstructed volume, axial and coronal slice were extracted and detection-SNR was calculated using channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels. Results: Detection-SNR of coronal images varies for different backprojection methods, while axial images have a similar detection-SNR. Detection-SNR{sup 2} ratios of coronal and axial images in RECON1 and RECON2 are 1.33 and 1.15, implying that the coronal image has a better detectability than axial image. In other words, using coronal slices for the small target detection can reduce the patient dose about 33% and 15% compared to using axial slices in RECON 1 and RECON 2. Conclusion: In this work, we investigated slice direction dependent detectability of a volumetric cone beam CT image. RECON 1 and RECON 2 produced the highest detection-SNR, with better detectability in coronal slices. These results indicate that it is more beneficial to use coronal slice to improve detectability of a small target in a volumetric cone beam CT image. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Program (NIPA-2014-H0201-14-1002) supervised by the NIPA (National IT Industry Promotion Agency). Authors declares that s/he has no conflict of Interest in relation to the work in this abstract.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-G-BRD-01: Quantifying the Effectiveness of the Physics Pre-Treatment Plan Review for Detecting Errors in Radiation Therapy

Purpose: Physics pre-treatment plan review is crucial to safe radiation oncology treatments. Studies show that most errors originate in treatment planning, which underscores the importance of physics plan review. As a QA measure the physics review is of fundamental importance and is central to the profession of medical physics. However, little is known about its effectiveness. More hard data are needed. The purpose of this study was to quantify the effectiveness of physics review with the goal of improving it. Methods: This study analyzed 315 “potentially serious” near-miss incidents within an institutional incident learning system collected over a two-year period. 139 of these originated prior to physics review and were found at the review or after. Incidents were classified as events that: 1)were detected by physics review, 2)could have been detected (but were not), and 3)could not have been detected. Category 1 and 2 events were classified by which specific check (within physics review) detected or could have detected the event. Results: Of the 139 analyzed events, 73/139 (53%) were detected or could have been detected by the physics review; although, 42/73 (58%) were not actually detected. 45/73 (62%) errors originated in treatment planning, making physics review the first step in the workflow that could detect the error. Two specific physics checks were particularly effective (combined effectiveness of >20%): verifying DRRs (8/73) and verifying isocenter (7/73). Software-based plan checking systems were evaluated and found to have potential effectiveness of 40%. Given current data structures, software implementations of some tests such as isocenter verification check would be challenging. Conclusion: Physics plan review is a key safety measure and can detect majority of reported events. However, a majority of events that potentially could have been detected were NOT detected in this study, indicating the need to improve the performance of physics review.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using an active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

GEDet: Detecting Erroneous Nodes with A Few Examples

Detecting nodes with erroneous values in real-world graphs re- mains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection. Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios. GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily “plug in” new error types or examples. (3) User-centric detection. GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.

Guan, Sheng↗

DOE OSTI2017

SISGR: Room Temperature Single-Molecule Detection and Imaging by Stimulated Emission Microscopy

Single-molecule spectroscopy has made considerable impact on many disciplines including chemistry, physics, and biology. To date, most single-molecule spectroscopy work is accomplished by detecting fluorescence. On the other hand, many naturally occurring chromophores, such as retinal, hemoglobin and cytochromes, do not have detectable fluorescence. There is an emerging need for single-molecule spectroscopy techniques that do not require fluorescence. In the last proposal period, we have successfully demonstrated stimulated emission microscopy, single molecule absorption, and stimulated Raman microscopy based on a high-frequency modulation transfer technique. These first-of-a- kind new spectroscopy/microscopy methods tremendously improved our ability to observe molecules that fluorescence weakly, even to the limit of single molecule detection for absorption measurement. All of these methods employ two laser beams: one (pump beam) excites a single molecule to a real or virtual excited state, and the other (probe beam) monitors the absorption/emission property of the single. We extract the intensity change of the probe beam with high sensitivity by implementing a high-frequency phase-sensitive detection scheme, which offers orders of magnitude improvement in detection sensitivity over direct absorption/emission measurement. However, single molecule detection based on fluorescence or absorption is fundamentally limited due to their broad spectral response. It is important to explore other avenues in single molecule detection and imaging which provides higher molecular specificity for studying a wide variety of heterogeneous chemical and biological systems. This proposal aimed to achieve single-molecule detection sensitivity with near resonance stimulated Raman scattering (SRS) microscopy. SRS microscopy was developed in our lab as a powerful technique for imaging heterogeneous samples based on their intrinsic vibrational contrasts, which provides much higher molecular specificity than absorption and fluorescence. Current sensitivity limit of SRS microscopy has not yet reached single molecule detection. We proposed to capitalize on our state-of-the-art SRS microscopy and develop near-resonance enhanced SRS for single molecule detection of carotenoids and heme proteins. The specific aims we pursued are: (1) building the next SRS generation microscope that utilizes near resonance enhancement to allow detection and imaging of single molecules with undetectable fluorescence, such as -carotene. (2) using near-resonance SRS as a contrast mechanism to study dye-sensitize semiconductor interface, elucidating the heterogeneous electron ejection kinetics with high spatial and temporal resolution. (3) studying the binding and unbinding of oxygen in single hemoglobin molecules in order to gain molecular level understanding of the long-standing issue of cooperativity. The new methods developed in the fund period of this grant have advanced the detection sensitivity in many aspects. Near-resonance SRS improved the signal by using shorter wavelengths for SRS microscopy. Frequency modulation and multi-color SRS target the reduction of background to improve the chemical specificity of SRS while maintaining the high imaging speed. Time-domain coherent Raman scattering microscopy targets to reduce the noise floor of coherent Raman microscopy. These methods have already demonstrated first-of-a-kind new applications in biology and medical research. However, we are still one order of magnitude away from single molecule limit. It is important to continue to improve the laser specification and develop new imaging methods to finally achieve label-free single molecule microscopy.

47 OTHER INSTRUMENTATION↗