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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Use of Automation in Low Level Waste Storage and Management - 19227

The Australian Nuclear Science and Technology Organisation (ANSTO) generates Low Level Waste (LLW) and Intermediate Level Waste (ILW) from various site operations such as the operation of a research Reactor and the manufacture of Molybdenum 99. The Low Level Solid Waste (LLSW) generated is stored at ANSTO before and after conditioning. The current process flow involves the storage of LLSW waste before conditioning on racks, handled using forklifts. When the unconditioned waste is ready for conditioning, it is transferred to another facility, where volume reduction and packaging is carried out. Once conditioned, the waste is placed in further storage, pending dispatch to the proposed Australian National Radioactive Waste Management Facility (NRWMF). A new purpose built facility has been constructed, adjacent to the LLSW processing facility, for the storage and management of LLSW in both unconditioned and conditioned forms. This new facility will streamline the waste management process and significantly enhance the safety and efficiency of the process. In line with international best practises, the new facility utilises automation solutions to minimise the build footprint, and minimise the use of forklifts and other mobile plant. Most importantly, it applies the principle of ALARA (As Low as Reasonably Achievable) to minimise operator activities in areas where radioactive material is stored and eliminates double handling of the waste. The automation for unconditioned waste utilises an Automated Storage and Retrieval System (ASRS), a vertical stacker crane within high density racking, to provide easy storage and retrieval for processing and/or routine inspections. This system allows operators to control it from outside of the storage area whilst utilising integrated software that allows for the required tracking of the unconditioned waste. From the ASRS storage area, unconditioned waste will have a smooth transition to the processing facility which is located adjacent to the ASRS system. Once conditioned, the waste is catalogued and returned to a designated storage area within the new facility using an automated building gantry crane with a target delivery system to eliminate the need for operators in the main storage bay. The waste will be held here in preparation for dispatch to the future repository. The utilisation of the ASRS for the purpose of storing LLSW is an innovative use of automation that will eliminate operators from active areas, reduce dose rates received and integrate with the waste tracking software. Furthermore, the system has significant advantages with operational efficiencies increased, reduced double handling of nuclear waste and maximum use of building footprint. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

A human automation interaction concept for a small modular reactor control room - 101

Many advanced nuclear power plant (NPP) designs incorporate higher degrees of automation than the existing fleet of NPPs. Automation is being introduced or proposed in NPPs through a wide variety of systems and technologies, such as advanced displays, computer-based procedures, advanced alarm systems, and computerized operator support systems. Research consistently finds that there is a fundamental tradeoff between system performance with increased automation and reduced human performance. Idaho National Laboratory (INL) researchers and NuScale Power human factors and operations staff are working on a collaborative project to address the human performance challenges of increased automation and to determine the principles that lead to optimal performance in highly automated systems. This paper will describe this concept in detail and will describe an experimental test of the concept. The benefits and challenges of the approach will be discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Effects of adaptive automation on operator-system performance - 103

Adaptive automation (AA) is the dynamic, real-time change in the degree of automation in response to situational changes. AA systems are being developed in many industries, including for nuclear power plant applications. AA is a promising means of mitigating human performance issues that often arise in highly automated systems. The purpose of this Nuclear Regulatory Commission study was to characterize important AA design features, define the effects of AA on performance, examine the human factors engineering (HFE) guidance available to design and evaluate AA systems, and identify research and development needs. In general, we found that AA improves task performance and situation awareness (SA) while managing workload. Although the research is limited, AA also supports operator recognition of automation failure and recovery. Research on human-system interfaces has identified principles showing promise for supporting SA, including failure detection, and limiting workload. Research further suggests that AA systems are both more effective, and less disruptive and distracting, when they follow rules of interaction similar to those used by human crewmembers. Our general conclusion about the HFE guidance available to designers and reviewers is that it is limited and has not kept pace with automation technology. We discuss several research and development needs that will help support a better understanding of where and how AA should be used and support the development of more comprehensive guidance for AA safety reviews. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Test case selection procedure for simulation-assisted automation testing - 321

Simulation-assisted automation testing is an efficient tool for validating the automation systems for nuclear power plants. This paper presented a systematic procedure for selecting test cases for simulation-assisted automation testing. The procedure is relies on the plant's licensing framework and allows associating the test cases to the functional requirements in order to produce traceable test set for validation. The procedure is demonstrated with a case study concerning the automation tests of Loviisa NPP's automation renewal project. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Quantifying the Mobility and Energy Benefits of Automated Mobility Districts Using Microscopic Traffic Simulation

Connected and automated vehicles (CAVs) are increasingly being discussed as the basis for shared mobility and on-demand services to replace privately owned vehicles. The rapid growth of transportation networking companies (TNCs) and their increasing investment in automated vehicle (AV) technologies attests to this. Combining the concepts of TNCs, with AV and on-demand transit services, the term 'automated mobility district' (AMD) describes a district-scale implementation of CAV technology to realize the full benefits of a shared, fully automated vehicle service within a confined region. This research effort provides a modeling architecture for AMDs along with a preliminary analysis to quantify the mobility and energy benefits of such districts. A customized open-source microscopic mobility simulation toolkit built on the simulation of urban mobility (SUMO) platform is implemented for AMD performance evaluation. Experimental scenarios are tested with different combinations of operational variables to provide insights on energy and mobility gains that can be realized in AMDs.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Rapid Automated Treatment Planning Process to Select Breast Cancer Patients for Active Breathing Control to Achieve Cardiac Dose Reduction

Purpose: To evaluate a rapid automated treatment planning process for the selection of patients with left-sided breast cancer for a moderate deep inspiration breath-hold (mDIBH) technique using active breathing control (ABC); and to determine the dose reduction to the left anterior descending coronary artery (LAD) and the heart using mDIBH. Method and Materials: Treatment plans were generated using an automated method for patients undergoing left-sided breast radiotherapy (n = 53) with two-field tangential intensity-modulated radiotherapy. All patients with unfavorable cardiac anatomy, defined as having >10 cm{sup 3} of the heart receiving 50% of the prescribed dose (V{sub 50}) on the free-breathing automated treatment plan, underwent repeat scanning on a protocol using a mDIBH technique and ABC. The doses to the LAD and heart were compared between the free-breathing and mDIBH plans. Results: The automated planning process required approximately 9 min to generate a breast intensity-modulated radiotherapy plan. Using the dose-volume criteria, 20 of the 53 patients were selected for ABC. Significant differences were found between the free-breathing and mDIBH plans for the heart V{sub 50} (29.9 vs. 3.7 cm{sup 3}), mean heart dose (317 vs. 132 cGy), mean LAD dose (2,047 vs. 594 cGy), and maximal dose to 0.2 cm{sup 3} of the LAD (4,155 vs. 1,507 cGy, all p <.001). Of the 17 patients who had a breath-hold threshold of {>=}0.8 L, 14 achieved a {>=}90% reduction in the heart V{sub 50} using the mDIBH technique. The 3 patients who had had a breath-hold threshold <0.8 L achieved a lower, but still significant, reduction in the heart V{sub 50}. Conclusions: A rapid automated treatment planning process can be used to select patients who will benefit most from mDIBH. For selected patients with unfavorable cardiac anatomy, the mDIBH technique using ABC can significantly reduce the dose to the LAD and heart, potentially reducing the cardiac risks.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

A Fully Automated Method for CT-on-Rails-Guided Online Adaptive Planning for Prostate Cancer Intensity Modulated Radiation Therapy

Purpose: This study was designed to validate a fully automated adaptive planning (AAP) method which integrates automated recontouring and automated replanning to account for interfractional anatomical changes in prostate cancer patients receiving adaptive intensity modulated radiation therapy (IMRT) based on daily repeated computed tomography (CT)-on-rails images. Methods and Materials: Nine prostate cancer patients treated at our institution were randomly selected. For the AAP method, contours on each repeat CT image were automatically generated by mapping the contours from the simulation CT image using deformable image registration. An in-house automated planning tool incorporated into the Pinnacle treatment planning system was used to generate the original and the adapted IMRT plans. The cumulative dose–volume histograms (DVHs) of the target and critical structures were calculated based on the manual contours for all plans and compared with those of plans generated by the conventional method, that is, shifting the isocenters by aligning the images based on the center of the volume (COV) of prostate (prostate COV-aligned). Results: The target coverage from our AAP method for every patient was acceptable, while 1 of the 9 patients showed target underdosing from prostate COV-aligned plans. The normalized volume receiving at least 70 Gy (V{sub 70}), and the mean dose of the rectum and bladder were reduced by 8.9%, 6.4 Gy and 4.3%, 5.3 Gy, respectively, for the AAP method compared with the values obtained from prostate COV-aligned plans. Conclusions: The AAP method, which is fully automated, is effective for online replanning to compensate for target dose deficits and critical organ overdosing caused by interfractional anatomical changes in prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Automated defect analysis in electron microscopic images

Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. Furthermore, these promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Does one need to polish electrodes in an eight pattern? Automation provides the answer

Automation of electrochemical measurements can accelerate the discovery of new electroactive materials. One of the hurdles to automated electrochemical measurement is the pretreatment of electrodes because mechanical polishing is usually conducted manually. Here we investigate the automation of electrochemical measurements using a robotic arm. We demonstrate automated mechanical polishing using a station with a moving polishing pad and evaluate the effect of different polishing patterns. Our automatic method improved the corroded electrodes, and we found the effect of pattern was not significant, which diverges from the current common belief amongst practitioners that a figure eight pattern is best for pretreatment. This research is a step toward automating electrochemistry experiments without human intervention.

Yoshikawa, Naruki [University of Toronto, ON (Cana↗
DOE OSTI2023

TDCOSMO. X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars with GLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of the Hubble Space Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling software Lenstronomy revealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗
DOE OSTI2014

SU-E-I-94: Automated Image Quality Assessment of Radiographic Systems Using An Anthropomorphic Phantom

Purpose: In a large, academic medical center, consistent radiographic imaging performance is difficult to routinely monitor and maintain, especially for a fleet consisting of multiple vendors, models, software versions, and numerous imaging protocols. Thus, an automated image quality control methodology has been implemented using routine image quality assessment with a physical, stylized anthropomorphic chest phantom. Methods: The “Duke” Phantom (Digital Phantom 07-646, Supertech, Elkhart, IN) was imaged twice on each of 13 radiographic units from a variety of vendors at 13 primary care clinics. The first acquisition used the clinical PA chest protocol to acquire the post-processed “FOR PRESENTATION” image. The second image was acquired without an antiscatter grid followed by collection of the “FOR PROCESSING” image. Manual CNR measurements were made from the largest and thickest contrast-detail inserts in the lung, heart, and abdominal regions of the phantom in each image. An automated image registration algorithm was used to estimate the CNR of the same insert using similar ROIs. Automated measurements were then compared to the manual measurements. Results: Automatic and manual CNR measurements obtained from “FOR PRESENTATION” images had average percent differences of 0.42%±5.18%, −3.44%±4.85%, and 1.04%±3.15% in the lung, heart, and abdominal regions, respectively; measurements obtained from “FOR PROCESSING” images had average percent differences of -0.63%±6.66%, −0.97%±3.92%, and −0.53%±4.18%, respectively. The maximum absolute difference in CNR was 15.78%, 10.89%, and 8.73% in the respective regions. In addition to CNR assessment of the largest and thickest contrast-detail inserts, the automated method also provided CNR estimates for all 75 contrast-detail inserts in each phantom image. Conclusion: Automated analysis of a radiographic phantom has been shown to be a fast, robust, and objective means for assessing radiographic image quality. The method reduces the burden of manual measurements and provides a means by which to monitor and compare radiographic system performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-45: An Automated Technique to Measure Image Contrast in Clinical CT Images

Purpose: To develop and validate an automated technique for measuring image contrast in chest computed tomography (CT) exams. Methods: An automated computer algorithm was developed to measure the distribution of Hounsfield units (HUs) inside four major organs: the lungs, liver, aorta, and bones. These organs were first segmented or identified using computer vision and image processing techniques. Regions of interest (ROIs) were automatically placed inside the lungs, liver, and aorta and histograms of the HUs inside the ROIs were constructed. The mean and standard deviation of each histogram were computed for each CT dataset. Comparison of the mean and standard deviation of the HUs in the different organs provides different contrast values. The ROI for the bones is simply the segmentation mask of the bones. Since the histogram for bones does not follow a Gaussian distribution, the 25th and 75th percentile were computed instead of the mean. The sensitivity and accuracy of the algorithm was investigated by comparing the automated measurements with manual measurements. Fifteen contrast enhanced and fifteen non-contrast enhanced chest CT clinical datasets were examined in the validation procedure. Results: The algorithm successfully measured the histograms of the four organs in both contrast and non-contrast enhanced chest CT exams. The automated measurements were in agreement with manual measurements. The algorithm has sufficient sensitivity as indicated by the near unity slope of the automated versus manual measurement plots. Furthermore, the algorithm has sufficient accuracy as indicated by the high coefficient of determination, R2, values ranging from 0.879 to 0.998. Conclusion: Patient-specific image contrast can be measured from clinical datasets. The algorithm can be run on both contrast enhanced and non-enhanced clinical datasets. The method can be applied to automatically assess the contrast characteristics of clinical chest CT images and quantify dependencies that may not be captured in phantom data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-201-03: Automation of High Dose Rate Brachytherapy Quality Assurance: Development of a Radioluminescent Detection System for Simultaneous Detection of Activity, Timing, and Positioning

Purpose: Accuracy of positioning, timing and activity is of critical importance for High Dose Rate (HDR) brachytherapy delivery. Respective measurements via film autoradiography, stop-watches and well chambers can be cumbersome, crude or lack dynamic source evaluation capabilities. To address such limitations, a single device radioluminescent detection system enabling automated real-time quantification of activity, position and timing accuracy is presented and experimentally evaluated. Methods: A radioluminescent sheet was fabricated by mixing Gd?O?S:Tb with PDMS and incorporated into a 3D printed device where it was fixated below a CMOS digital camera. An Ir-192 HDR source (VS2000, VariSource iX) with an effective active length of 5 mm was introduced using a 17-gauge stainless steel needle below the sheet. Pixel intensity values for determining activity were taken from an ROI centered on the source location. A calibration curve relating intensity values to activity was generated and used to evaluate automated activity determination with data gathered over 6 weeks. Positioning measurements were performed by integrating images for an entire delivery and fitting peaks to the resulting profile. Timing measurements were performed by evaluating source location and timestamps from individual images. Results: Average predicted activity error over 6 weeks was .35 ± .5%. The distance between four dwell positions was determined by the automated system to be 1.99 ± .02 cm. The result from autoradiography was 2.00 ± .03 cm. The system achieved a time resolution of 10 msec and determined the dwell time to be 1.01 sec ± .02 sec. Conclusion: The system was able to successfully perform automated detection of activity, positioning and timing concurrently under a single setup. Relative to radiochromic and radiographic film-based autoradiography, which can only provide a static evaluation positioning, optical detection of temporary radiation induced luminescence enables dynamic detection of position enabling automated quantification of timing with millisecond accuracy.

60 APPLIED LIFE SCIENCES↗