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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-160: Characterization and Monitoring of Linear Accelerator Gantry Radiation Isocenter Motion

Purpose: To characterize the motion of the radiation isocenter, over time, as a function of gantry rotation for multiple linear accelerators (linacs). Two semi-automated image-based quality control (QC) test workflows were designed to achieve this goal. Methods: The full QC-test workflow for motion characterization consisted of acquiring 16 megavoltage images at 8 gantry angles of a ball-bearing suspended off the end of the couch. Performance constancy was assessed using a shortened QC-test workflow which consisted of imaging a cube phantom placed on the couch (5 images at 4 gantry angles). Both workflows use an image processing algorithm to determine the field center and phantom position on each image and computed radiation isocenter motion as a function of gantry angle. Motion was characterized for 9 linacs of same model and performance monitored for 2 months on 3 linacs. Results: The maximum isocenter motion determined with the full-workflow for 9 linacs was within 0.38–0.79 mm. The shortened-workflow usually agreed within 0.1 mm with the full-workflow and the time required for these methods was about 4 and 15 min, respectively. For all linacs, the isocenter motion perpendicular to the gantry rotation plane followed a consistent pattern with maximum amplitude of 0.36–0.59 mm. In the gantry rotation plane, the variation among linacs was higher and the beam axis described a circle of up to 0.6 mm radius around the gantry axis of rotation (2 linacs). The radiation isocenter motion was stable as a function of time for the monitored linacs and was within ±0.1 mm of the average. Conclusion: Radiation isocenter motion parallel and perpendicular to the gantry rotation plane was characterized. In the gantry rotation plane, beam spot positioning adjustment might be used to reduce the observed radiation isocenter motion. A shortened-workflow was designed and enables performance monitoring over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-D-213-01: Workflow Monitoring for a High Volume Radiation Oncology Center

Purpose: Implement a center wide communication system that increases interdepartmental transparency and accountability while decreasing redundant work and treatment delays by actively monitoring treatment planning workflow. Methods: Intake Management System (IMS), a program developed by ProCure Treatment Centers Inc., is a multi-function database that stores treatment planning process information. It was devised to work with the oncology information system (Mosaiq) to streamline interdepartmental workflow.Each step in the treatment planning process is visually represented and timelines for completion of individual tasks are established within the software. The currently active step of each patient’s planning process is highlighted either red or green according to whether the initially allocated amount of time has passed for the given process. This information is displayed as a Treatment Planning Process Monitor (TPPM), which is shown on screens in the relevant departments throughout the center. This display also includes the individuals who are responsible for each task.IMS is driven by Mosaiq’s quality checklist (QCL) functionality. Each step in the workflow is initiated by a Mosaiq user sending the responsible party a QCL assignment. IMS is connected to Mosaiq and the sending or completing of a QCL updates the associated field in the TPPM to the appropriate status. Results: Approximately one patient a week is identified during the workflow process as needing to have his/her treatment start date modified or resources re-allocated to address the most urgent cases. Being able to identify a realistic timeline for planning each patient and having multiple departments communicate their limitations and time constraints allows for quality plans to be developed and implemented without overburdening any one department. Conclusion: Monitoring the progression of the treatment planning process has increased transparency between departments, which enables efficient communication. Having built-in timelines allows easy prioritization of tasks and resources and facilitates effective time management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-I-01: EIT Imaging to Monitor Human Salivary Gland Functionality: A Feasibility Study

Purpose: Clinically, there exists a need to develop a non-invasive technique for monitoring salivary activity. In this study, we investigate the feasibility of a using the electrical conductivity information from Electrical Impedance Tomography (EIT) to monitor salivary flow activity. Methods: To acquire EIT data, eight Ag/AgCl ECG electrodes were placed around the mandible of the subject. An EIT scan was obtained by injecting current at 50 KHz, 0.4 mA through each pair of electrodes and recording voltage across other electrode pairs. The functional conductivity image was obtained through reconstruction of the voltage data, using Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) in Matlab. In using EIDORS, forward solution was obtained using a user-defined finite element model shape and inverse solution was obtained using one-step Gaussian solver. EIT scans of volunteer research team members were acquired for three different physiological states: pre-stimulation, stimulation and post-stimulation. For pre-stimulation phase, data were collected in intervals of 5 minutes for 15 minutes. The salivary glands were then stimulated in the subject using lemon and the data were collected immediately. Post-stimulation data were collected at 4 different timings after stimulation. Results: Variations were observed in the electrical conductivity patterns near parotid regions between the pre- and post-stimulation stages. The three images acquired during the 15 minute pre-stimulation phase showed no major changes in the conductivity. Immediately after stimulation, electrical conductivity increased near parotid regions and 15 minutes later slowly returned to pre-stimulation level. Conclusion: In the present study involving human subjects, the change in electrical conductivity pattern shown in the EIT images, acquired at different times with and without stimulation of salivary glands, appeared to be consistent with the change in salivary gland activity. The conductivity changes imaged through EIT are potentially useful for the purpose of salivary monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-210-03: Real-Time Ablation Monitoring and Lesion Quantification Using Harmonic Motion Imaging

The use of therapeutic ultrasound to provide targeted therapy is an active research area that has a broad application scope. The invited talks in this session will address currently implemented strategies and protocols for both hyperthermia and ablation applications using therapeutic ultrasound. The role of both ultrasound and MRI in the monitoring and assessment of these therapies will be explored in both pre-clinical and clinical applications. Katherine Ferrara: High Intensity Focused Ultrasound, Drug Delivery, and Immunotherapy Rajiv Chopra: Translating Localized Doxorubicin Delivery to Pediatric Oncology using MRI-guided HIFU Elisa Konofagou: Real-time Ablation Monitoring and Lesion Quantification using Harmonic Motion Imaging Keyvan Farahani: AAPM Task Groups in Interventional Ultrasound Imaging and Therapy Learning Objectives: Understand the role of ultrasound in localized drug delivery and the effects of immunotherapy when used in conjunction with ultrasound therapy. Understand potential targeted drug delivery clinical applications including pediatric oncology. Understand the technical requirements for performing targeted drug delivery. Understand how radiation-force approaches can be used to both monitor and assess high intensity focused ultrasound ablation therapy. Understand the role of AAPM task groups in ultrasound imaging and therapies. Chopra: Funding from Cancer Prevention and Research Initiative of Texas (CPRIT), Award R1308 Evelyn and M.R. Hudson Foundation; Research Support from Research Contract with Philips Healthcare; COI are Co-founder of FUS Instruments Inc Ferrara: Supported by NIH, UCDavis and California (CIRM and BHCE) Farahani: In-kind research support from Philips Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-201-03: During-Treatment Delivery Monitoring System for TomoTherapy

Purpose: Multiple error pathways can lead to delivery errors during the treatment course that cannot be caught with pre-treatment QA. While in vivo solutions are being developed for linacs, no such solution exists for tomotherapy. The purpose of this study is to develop a near real-time system for tomotherapy that can monitor the delivery and dose accumulation process during the treatment-delivery, which enable the user to assess the impact of delivery variations and/or errors and to interrupt the treatment if necessary. Methods: A program running on a tomotherapy planning station fetches the raw DAS data during treatment. Exit detector data is extracted as well as output, gantry angle, and other machine parameters. For each sample, the MLC open-close state is determined. The delivered plan is compared with the original plan via a Monte Carlo dose engine which transports fluence deviations from a pre-treatment Monte Carlo run. A report containing the difference in fluence, dose and DVH statistics is created in html format. This process is repeated until the treatment is completed. Results: Since we only need to compute the dose for the difference in fluence for a few projections each time, dose with 2% statistical uncertainty can be computed in less than 1 second on a 4-core cpu. However, the current bottleneck in this near real-time system is the repeated fetching and processing the growing DAS data file throughout the delivery. The frame rate drops from 10Hz at the beginning of treatment to 5Hz after 3 minutes and to 2Hz after 10 minutes. Conclusion: A during-treatment delivery monitor system has been built to monitor tomotherapy treatments. The system improves patient safety by allowing operators to assess the delivery variations and errors during treatment delivery and adopt appropriate actions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-67: Correction Factors for Monitor Unit Verification of Clinical Electron Beams

Purpose: Monitor units calculated by electron Monte Carlo treatment planning systems are often higher than TG-71 hand calculations for a majority of patients. Here I’ve calculated tables of geometry and heterogeneity correction factors for correcting electron hand calculations. Method: A flat water phantom with spherical volumes having radii ranging from 3 to 15 cm was created. The spheres were centered with respect to the flat water phantom, and all shapes shared a surface at 100 cm SSD. D{sub max} dose at 100 cm SSD was calculated for each cone and energy on the flat phantom and for the spherical volumes in the absence of the flat phantom. The ratio of dose in the sphere to dose in the flat phantom defined the geometrical correction factor. The heterogeneity factors were then calculated from the unrestricted collisional stopping power for tissues encountered in electron beam treatments. These factors were then used in patient second check calculations. Patient curvature was estimated by the largest sphere that aligns to the patient contour, and appropriate tissue density was read from the physical properties provided by the CT. The resulting MU were compared to those calculated by the treatment planning system and TG-71 hand calculations. Results: The geometry and heterogeneity correction factors range from ∼(0.8–1.0) and ∼(0.9–1.01) respectively for the energies and cones presented. Percent differences for TG-71 hand calculations drop from ∼(3–14)% to ∼(0–2)%. Conclusion: Monitor units calculated with the correction factors typically decrease the percent difference to under actionable levels, < 5%. While these correction factors work for a majority of patients, there are some patient anatomies that do not fit the assumptions made. Using these factors in hand calculations is a first step in bringing the verification monitor units into agreement with the treatment planning system MU.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-330: Characterization of the Clinically Released ScandiDos Discover Diode Array for In-Vivo Dose Monitoring

Purpose: The ScandiDos Discover has obtained FDA clearance and is now clinically released. We studied the essential attenuation and beam hardening components as well as tested the diode array’s ability to detect changes in absolute dose and MLC leaf positions. Methods: The ScandiDos Discover was mounted on the heads of an Elekta VersaHD and a Varian 23EX. Beam attenuation measurements were made at 10 cm depth for 6 MV and 18 MV beam energies. The PDD(10) was measured as a metric for the effect on beam quality. Next, a plan consisting of two orthogonal 10 × 10 cm2 fields was used to adjust the dose per fraction by scaling monitor units to test the absolute dose detection sensitivity of the Discover. A second plan (conformal arc) was then delivered several times independently on the Elekta VersaHD. Artificially introduced MLC position errors in the four central leaves were then added. The errors were incrementally increased from 1 mm to 4 mm and back across seven control points. Results: The absolute dose measured at 10 cm depth decreased by 1.2% and 0.7% for 6 MV and 18 MV beam with the Discover, respectively. Attenuation depended slightly on the field size but only changed the attenuation by 0.1% across 5 × 5 cm{sup 2} and 20 − 20 cm{sup 2} fields. The change in PDD(10) for a 10 − 10 cm{sup 2} field was +0.1% and +0.6% for 6 MV and 18 MV, respectively. Changes in monitor units from −5.0% to 5.0% were faithfully detected. Detected leaf errors were within 1.0 mm of intended errors. Conclusion: A novel in-vivo dosimeter monitoring the radiation beam during treatment was examined through its attenuation and beam hardening characteristics. The device tracked with changes in absolute dose as well as introduced leaf position deviations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SierraNet: monitoring the snowpack in the Sierra Nevada: demo

© 2016 Copyright held by the owner/author(s). Next-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture, air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring.

Brun-Laguna, Keoma↗
DOE OSTI2025

Bridging the Gap: User-Centric Energy Monitoring for Policy-Driven Application Optimization in HPC Data Centers

Application energy optimization in HPC data centers face two critical gaps. Systematic methodologies that connect data center policies to application decisions and accessible monitoring tools that enable data-driven optimization. We address both gaps through two complementary pillars. First, we present a methodology based on extended weighted Energy Delay Product (EDP) to translate data center operational priorities and integrate energy considerations into the energy optimization workflow which starts from continuous monitoring through targeted optimization. Second, we present a user-space monitoring tool, Omnistat, that enables this methodology by providing developers with direct access to actionable energy telemetry. Through deployment on the Frontier supercomputer and case studies exploring performance-energy trade-offs, we show how these pillars help energy as an integral optimization target for developers as active participants in data center efficiency.

Shin, Woong [ORNL] (ORCID:0000000172077814)↗
DOE OSTI2019

Electrochemical Sensors for Air Quality Monitoring

We report the continuous growth of modern industries has left the world with poor air quality especially in urban areas and work place environments, posing a major challenge to quality of life. The major contributors to traditional air pollution are NOx, SOx, and H2S while NH3 and Volatile organic compounds are of increasing concern recently. Traditional air quality monitors based on mass spec, infra-red spectroscopy and gas chromatography are expensive and not suitable for large scale deployment. Electrochemical gas sensors provide a cheap alternative option for widespread air quality monitoring. While many electrochemical sensors detect toxic gases in the range of 1 – 10,000 ppm, recent advances in chemiresistive and mixed potential electrochemical sensors have brought down the detection range to parts per billion, required range for environmental monitoring. In this Interface article, we discuss recent advancements in sensors for the efficient detection of key air pollutant gases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Nekton Interaction Monitoring System

The software provides a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) extracts and archives tracking and backscatter statistics data from a real-time stream of data from a sonar device. NIMS also sends real-time tracking messages over the network that can be used by other systems to generate other metrics or to trigger instruments such as an optical video camera. A web-based user interface provides remote monitoring and control. NIMS currently supports three popular sonar devices: M3 multi-beam sonar (Kongsberg), EK60 split-beam echo-sounder (Simrad) and BlueView acoustic camera (Teledyne).

Source record↗
DOE OSTI2022

Lab-on-a-Fish Monitoring Software V1.0

This is the original monitoring software for processing and displaying data for Lab-on-a-Fish. Lab-on-a-Fish simultaneously evaluates and monitors the health, behavior, and environment of fish or small aquatic species. The sensor is a miniaturized underwater monitoring system that measures animal behavior (acceleration and gyration); physiology (electrocardiogram and electromyogram); and environmental parameters (temperature, pressure, and magnetic sensor).

Deng, Daniel↗
DOE OSTI2016

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. Furthermore, a quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

An all fiber-optic multi-parameter structure health monitoring system

In this article, we present an all fiber-optics based multi-parameter structure health monitoring system, which is able to monitor strain, temperature, crack and thickness of metal structures. This system is composed of two optical fibers, one for laser-acoustic excitation and the other for acoustic detection. A nano-second 1064 nm pulse laser was used for acoustic excitation and a 2 mm fiber Bragg grating was used to detect the acoustic vibration. The feasibility of this system was demonstrated on an aluminum test piece by the monitoring of the temperature, strain and thickness changes, as well as the appearance of an artificial crack. The multiplexing capability of this system was also preliminarily demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Wrapper to Use a Machine-Learning-Based Algorithm for Earthquake Monitoring

Seismology is one of the main sciences used to monitor volcanic activity worldwide. Fast, efficient, and accurate seismicity detectors are crucial to assess the activity level of a volcano in near–real time and to issue timely warnings. Traditional real–time seismic processing software uses phase onset pickers followed by a phase association algorithm to declare an event and estimate its location. The pickers typically do not identify whether the detected phase is a P or S arrival, which can have a negative impact on hypocentral location quality and complicates phase association. We implemented the deep–neural–network–based method PhaseNet to identify in real time P and S seismic waves on data from one– and three–component seismometers. We tuned the Earthworm binder_ew associator module to use the phase identification from PhaseNet to detect and locate the events, which we archive in a SeisComP3 database. We assessed the performance of the algorithm by comparing the results with existing catalogs built to monitor seismic and volcanic activity in Mayotte and the Lesser Antilles region. Our algorithm, which we refer to as PhaseWorm, showed promising results in both contexts and clearly outperformed the previous automatic method implemented in Mayotte. As a result, this innovative real–time processing system is now operational for seismicity monitoring in Mayotte and Martinique.

58 GEOSCIENCES↗
DOE OSTI2025

Distributed Acoustic Sensing for Whale Vocalization Monitoring: A Vertical Deployment Field Test

Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments.

Saw, Jaewon↗