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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Automating linear accelerator quality assurance

Purpose: The purpose of this study was 2-fold. One purpose was to develop an automated, streamlined quality assurance (QA) program for use by multiple centers. The second purpose was to evaluate machine performance over time for multiple centers using linear accelerator (Linac) log files and electronic portal images. The authors sought to evaluate variations in Linac performance to establish as a reference for other centers. Methods: The authors developed analytical software tools for a QA program using both log files and electronic portal imaging device (EPID) measurements. The first tool is a general analysis tool which can read and visually represent data in the log file. This tool, which can be used to automatically analyze patient treatment or QA log files, examines the files for Linac deviations which exceed thresholds. The second set of tools consists of a test suite of QA fields, a standard phantom, and software to collect information from the log files on deviations from the expected values. The test suite was designed to focus on the mechanical tests of the Linac to include jaw, MLC, and collimator positions during static, IMRT, and volumetric modulated arc therapy delivery. A consortium of eight institutions delivered the test suite at monthly or weekly intervals on each Linac using a standard phantom. The behavior of various components was analyzed for eight TrueBeam Linacs. Results: For the EPID and trajectory log file analysis, all observed deviations which exceeded established thresholds for Linac behavior resulted in a beam hold off. In the absence of an interlock-triggering event, the maximum observed log file deviations between the expected and actual component positions (such as MLC leaves) varied from less than 1% to 26% of published tolerance thresholds. The maximum and standard deviations of the variations due to gantry sag, collimator angle, jaw position, and MLC positions are presented. Gantry sag among Linacs was 0.336 ± 0.072 mm. The standard deviation in MLC position, as determined by EPID measurements, across the consortium was 0.33 mm for IMRT fields. With respect to the log files, the deviations between expected and actual positions for parameters were small (<0.12 mm) for all Linacs. Considering both log files and EPID measurements, all parameters were well within published tolerance values. Variations in collimator angle, MLC position, and gantry sag were also evaluated for all Linacs. Conclusions: The performance of the TrueBeam Linac model was shown to be consistent based on automated analysis of trajectory log files and EPID images acquired during delivery of a standardized test suite. The results can be compared directly to tolerance thresholds. In addition, sharing of results from standard tests across institutions can facilitate the identification of QA process and Linac changes. These reference values are presented along with the standard deviation for common tests so that the test suite can be used by other centers to evaluate their Linac performance against those in this consortium.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

Purpose: Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. Methods: kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Results: Regular dual-energy imaging was able to increase tracking accuracy in left–right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. Conclusions: This study has highlighted the influence of patient anatomy on the success rate of real-time markerless tumor tracking using dual-energy imaging. Additionally, the importance of the spectral separation of the imaging beams used to generate the dual-energy images has been shown.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction

Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. Results: There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. Conclusions: The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-BRB-05: Automation of the Photon Dosimetric Quality Assurance Program of a Linear Accelerator

Purpose: To develop an automated method to calculate a linear accelerator (LINAC) photon radiation field size, flatness, symmetry, output and beam quality in a single delivery for flattened (FF) and flattening-filter-free (FFF) beams using an ionization chamber array. Methods: The proposed method consists of three control points that deliver 30×30, 10×10 and 5×5cm{sup 2} fields (FF or FFF) in a step-and-shoot sequence where the number of monitor units is weighted for each field size. The IC Profiler (Sun Nuclear Inc.) with 5mm detector spacing was used for this study. The corrected counts (CCs) were calculated and the locations of the maxima and minima values of the first-order gradient determined data of each sub field. Then, all CCs for each field size are summed in order to obtain the final profiles. For each profile, the radiation field size, symmetry, flatness, output factor and beam quality were calculated. For field size calculation, a parameterized gradient method was used. For method validation, profiles were collected in the detector array both, individually and as part of the step-and-shoot plan, with 9.9cm buildup for FF and FFF beams at 90cm source-to-surface distance. The same data were collected with the device (plus buildup) placed on a movable platform to achieve a 1mm resolution. Results: The differences between the dosimetric quantities calculated from both deliveries, individually and step-and-shoot, were within 0.31±0.20% and 0.04±0.02mm. The differences between the calculated field sizes with 5mm and 1mm resolution were ±0.1mm. Conclusion: The proposed single delivery method proved to be simple and efficient in automating the photon dosimetric monthly and annual quality assurance.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-G-206-17: RadShield: Semi-Automated Shielding Design for CT Using NCRP 147 and Isodose Curves

Purpose: Computed tomography (CT) exam rooms are shielded more quickly and accurately compared to manual calculations using RadShield, a semi-automated diagnostic shielding software package. Last year, we presented RadShield’s approach to shielding radiographic and fluoroscopic rooms calculating air kerma rate and barrier thickness at many points on the floor plan and reporting the maximum values for each barrier. RadShield has now been expanded to include CT shielding design using not only NCRP 147 methodology but also by overlaying vendor provided isodose curves onto the floor plan. Methods: The floor plan image is imported onto the RadShield workspace to serve as a template for drawing barriers, occupied regions and CT locations. SubGUIs are used to set design goals, occupancy factors, workload, and overlay isodose curve files. CTDI and DLP methods are solved following NCRP 147. RadShield’s isodose curve method employs radial scanning to extract data point sets to fit kerma to a generalized power law equation of the form K(r) = ar^b. RadShield’s semiautomated shielding recommendations were compared against a board certified medical physicist’s design using dose length product (DLP) and isodose curves. Results: The percentage error found between the physicist’s manual calculation and RadShield’s semi-automated calculation of lead barrier thickness was 3.42% and 21.17% for the DLP and isodose curve methods, respectively. The medical physicist’s selection of calculation points for recommending lead thickness was roughly the same as those found by RadShield for the DLP method but differed greatly using the isodose method. Conclusion: RadShield improves accuracy in calculating air-kerma rate and barrier thickness over manual calculations using isodose curves. Isodose curves were less intuitive and more prone to error for the physicist than inverse square methods. RadShield can now perform shielding design calculations for general scattering bodies for which isodose curves are provided.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

TH-AB-BRA-02: Automated Triplet Beam Orientation Optimization for MRI-Guided Co-60 Radiotherapy

Purpose: MRI guided Co-60 provides daily and intrafractional MRI soft tissue imaging for improved target tracking and adaptive radiotherapy. To remedy the low output limitation, the system uses three Co-60 sources at 120° apart, but using all three sources in planning is considerably unintuitive. We automate the beam orientation optimization using column generation, and then solve a novel fluence map optimization (FMO) problem while regularizing the number of MLC segments. Methods: Three patients—1 prostate (PRT), 1 lung (LNG), and 1 head-and-neck boost plan (H&NBoost)—were evaluated. The beamlet dose for 180 equally spaced coplanar beams under 0.35 T magnetic field was calculated using Monte Carlo. The 60 triplets were selected utilizing the column generation algorithm. The FMO problem was formulated using an L2-norm minimization with anisotropic total variation (TV) regularization term, which allows for control over the number of MLC segments. Our Fluence Regularized and Optimized Selection of Triplets (FROST) plans were compared against the clinical treatment plans (CLN) produced by an experienced dosimetrist. Results: The mean PTV D95, D98, and D99 differ by −0.02%, +0.12%, and +0.44% of the prescription dose between planning methods, showing same PTV dose coverage. The mean PTV homogeneity (D95/D5) was at 0.9360 (FROST) and 0.9356 (CLN). R50 decreased by 0.07 with FROST. On average, FROST reduced Dmax and Dmean of OARs by 6.56% and 5.86% of the prescription dose. The manual CLN planning required iterative trial and error runs which is very time consuming, while FROST required minimal human intervention. Conclusions: MRI guided Co-60 therapy needs the output of all sources yet suffers from unintuitive and laborious manual beam selection processes. Automated triplet orientation optimization is shown essential to overcome the difficulty and improves the dosimetry. A novel FMO with regularization provides additional controls over the number of MLC segments and treatment time. Varian Medical Systems; NIH grant R01CA188300; NIH grant R43CA183390.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Poster - 09: A MATLAB-based Program for Automated Quality Assurance of a Prostate Brachytherapy Ultrasound System

Purpose: A robust Quality Assurance (QA) program is essential for prostate brachytherapy ultrasound systems due to the importance of imaging accuracy during treatment and planning. Task Group 128 of the American Association of Physicists in Medicine has recommended a set of QA tests covering grayscale visibility, depth of penetration, axial and lateral resolution, distance measurement, area measurement, volume measurement, and template/electronic grid alignment. Making manual measurements on the ultrasound system can be slow and inaccurate, so a MATLAB program was developed for automation of the described tests. Methods: Test images were acquired using a BK Medical Flex Focus 400 ultrasound scanner and 8848 transducer with the CIRS Brachytherapy QA Phantom – Model 045A. For each test, the program automatically segments the inputted image(s), makes the appropriate measurements, and indicates if the test passed or failed. The program was tested by analyzing two sets of images, where the measurements from the first set were used as baseline values. Results: The program successfully analyzed the images for each test and determined if any action limits were exceeded. All tests passed – the measurements made by the program were consistent and met the requirements outlined by Task Group 128. Conclusions: The MATLAB program we have developed can be used for automated QA of an ultrasound system for prostate brachytherapy. The GUI provides a user-friendly way to analyze images without the need for any manual measurement, potentially removing intra- and inter-user variability for more consistent results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Symmetry reduction of tensor networks in many-body theory: I. Automated symbolic evaluation of SU(2) algebra

Abstract The ongoing progress in (nuclear) many-body theory is accompanied by an ever-rising increase in complexity of the underlying formalisms used to solve the stationary Schrödinger equation. The associated working equations at play in state-of-the-art ab initio nuclear many-body methods can be analytically reduced with respect to angular-momentum, i.e. SU (2), quantum numbers whenever they are effectively employed in a symmetry-restricted context. The corresponding procedure constitutes a tedious and error-prone but yet an integral part of the implementation of those many-body frameworks. Indeed, this symmetry reduction is a key step to advance modern simulations to higher accuracy since the use of symmetry-adapted tensors can decrease the computational complexity by orders of magnitude. While attempts have been made in the past to automate the (anti-) commutation rules linked to Fermionic and Bosonic algebras at play in the derivation of the working equations, there is no systematic account to achieve the same goal for their symmetry reduction. In this work, the first version of an automated tool performing graph-theory-based angular-momentum reduction is presented. Taking the symmetry-unrestricted expressions of a generic tensor network as an input, the code provides their angular-momentum-reduced form in an error-safe way in a matter of seconds. Several state-of-the-art many-body methods serve as examples to demonstrate the generality of the approach and to highlight the potential impact on the many-body community.

Tichai, A.↗
DOE OSTI2018

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

COMPASS: A Framework for Automated Performance Modeling and Prediction

Flexible, accurate performance predictions offer numerous benefits such as gaining insight into and optimizing applications and architectures. However, the development and evaluation of such performance predictions has been a major research challenge, due to the architectural complexities. To address this challenge, we have designed and implemented a prototype system, named COMPASS, for automated performance model generation and prediction. COMPASS generates a structured performance model from the target application's source code using automated static analysis, and then, it evaluates this model using various performance prediction techniques. As we demonstrate on several applications, the results of these predictions can be used for a variety of purposes, such as design space exploration, identifying performance tradeoffs for applications, and understanding sensitivities of important parameters. COMPASS can generate these predictions across several types of applications from traditional, sequential CPU applications to GPU-based, heterogeneous, parallel applications. Our empirical evaluation demonstrates a maximum overhead of 4%, flexibility to generate models for 9 applications, speed, ease of creation, and very low relative errors across a diverse set of architectures.

Lee, Seyong↗
DOE OSTI2016

Energy Impact of Different Penetrations of Connected and Automated Vehicles: A Preliminary Assessment

Previous research reported in the literature has shown the benefits of traffic coordination to alleviate congestion, and reduce fuel consumption and emissions. However, there are still many remaining challenges that need to be addressed before a massive deployment of fully automated vehicles. This paper aims to investigate the energy impacts of different penetration rates of connected and automated vehicles (CAVs) and their interaction with human-driven vehicles. We develop a simulation framework for mixed traffic (CAVs interacting with human-driven vehicles) in merging roadways and analyze the energy impact of different penetration rates of CAVs on the energy consumption. The Gipps car following model is used along with heuristic controls to represent the driver decisions in a merging roadways traffic scenario. Using different penetration rates of CAVs, the simulation results indicated that for low penetration rates, the fuel consumption benefits are significant but the total travel time increases. The benefits in travel time are noticeable for higher penetration rates of CAVs.

Rios-Torres, Jackeline↗
DOE OSTI2021

Automated Testing System

The Automated Testing System (ATS) is an open-source, Python-based tool for automating the running of tests of an application across a broad range of high performance computers.

Dempsey, StephanieM.↗
DOE OSTI2019

SolarAPP+ ® (Solar Automated Permit Processing Plus) [SWR-19-51]

SolarAPP+ ®: Safe and affordable home solar through permitting automation. NREL has launched a collaborative effort with key code officials, authorities having jurisdiction (AHJs), and the solar industry to develop an instant online solar permitting platform for code compliant residential systems. SolarAPP (Solar Automated Permit Processing) will: -Provide a flexible, web-based PV-permitting tool for residential systems at no-cost to AHJs. -Enable standardization of instant permitting processes. -Evaluate applications for safety and code compliance. -Offer opportunities to incorporate energy storage and expand to other market segments.

Cook, Jeffrey↗
DOE OSTI2022

Security Catch and Release Automation Manager (SCRAM) v1.0

This is a security automation tool heavily influenced by the tool written at NCSA https://github.com/ncsa/bhr-site. The tool is a web interface that aims to centralize information on blocking malicious traffic. There is a list of currently blocked traffic, a place for other tools to automatically add traffic they deem malicious, as well as for human users to input blocks. * Logging and auditing, to determine who made what change and when * Automated expiration of changes (e.g. an exception is added for 2 weeks, after which time it will be removed) * Validation of changes and verification that the change has been applied * Fine-grained role-based access

Oehlert, Samuel↗
DOE OSTI2024

Machine Learning Automation Pipeline

Machine Learning Automation Pipeline (MLAP) is a package to perform machine learning (ML) analysis in a step by step manner, starting with data extraction until analysis and prediction. The scripts provide the users option to chose an action such as "Extract", "Prep", and "Train" and numerous cases can be launched with just a single command. The inputs for each case are provided using a JSON file. The simulation results of several cases can be assessed using an automated process and analyzed for various metrics pertinent to ML analysis.

Jha, Pankaj↗
DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗