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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing the Implications of Automated Merging Control in a Mixed and Heterogeneous Traffic Environment

Previous efforts to explore the implications of partial market penetration of connected and automated vehicles (CAVs) show a consensus on the benefits of higher market penetration rates (MPR) of vehicles enabled with connectivity and/or automation. There is, however, a level of uncertainty regarding the effects of lower market penetration rates and the consideration of heterogeneous vehicle fleets. Using VISSIM to perform microscopic traffic simulation and, vehicle simulation models, we assess the impacts of different CAVs market penetration rates on fuel consumption considering a heterogeneous traffic environment. The results show that the fuel efficiency benefits of optimal coordination control are maximized in moderate congested scenarios when the CAVs MPR exceeds 40%.

Rios Torres, Jackeline↗
DOE OSTI2020

The Application of Coordination to Magnetic Measurement Automation: An SSW System Example

Magnetic measurements are an integral part of the development cycle of accelerator magnets, a part which provides necessary feedback to drive design and manufacturing improvements and corrections. Automation of these measurements allows for reduction of human errors and reproducibility of measurements. Realization of automation via coordination that is separated at the architectural level from the functional code provides flexibility in defining measurement procedures and substantial extensibility. The described Single Stretched Wire (SSW) system herein is an implementation of such a solution with the use of a component-based framework designed to build a family of measurement systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. Here, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automating the interpretation of PM 2.5 time–resolved measurements using a data–driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM 2.5 time-resolved data and differentiating PM 2.5 emissions resulting from indoor and outdoor sources. Here, we use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM 2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2011

Automated noninvasive classification of renal cancer on multiphase CT

Purpose: To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions. Methods: A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphase registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves. Results: Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement. Conclusions: The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams. Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a ''reference standard'' was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours. Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 {+-} 9.7% s.d., and the average Hausdorff distance was 3.2 {+-} 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%. Conclusions: Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Software-controlled, highly automated intrafraction prostate motion correction with intrafraction stereographic targeting: System description and clinical results

Purpose: A new system for software-controlled, highly automated correction of intrafraction prostate motion,'' intrafraction stereographic targeting'' (iSGT), is described and evaluated. Methods: At our institute, daily prostate positioning is routinely performed at the start of treatment beam using stereographic targeting (SGT). iSGT was implemented by extension of the SGT software to facilitate fast and accurate intrafraction motion corrections with minimal user interaction. iSGT entails megavoltage (MV) image acquisitions with the first segment of selected IMRT beams, automatic registration of implanted markers, followed by remote couch repositioning to correct for intrafraction motion above a predefined threshold, prior to delivery of the remaining segments. For a group of 120 patients, iSGT with corrections for two nearly lateral beams was evaluated in terms of workload and impact on effective intrafraction displacements in the sagittal plane. Results: SDs of systematic ({Sigma}) and random ({sigma}) displacements relative to the planning CT measured directly after initial SGT setup correction were <0.5 and <0.8 mm, respectively. Without iSGT corrections, effective {Sigma} and {sigma} for the 11-min treatments would increase to {Sigma}{sub eff} < 1.1 mm and {sigma}{sub eff} < 1.2 mm. With the iSGT procedure with an action level of 4 mm, effective positioning errors were reduced to {Sigma}{sub eff} < 0.8 mm and {sigma}{sub eff} < 1.0 mm, with 23.1% of all fractions requiring a correction. Computer simulations demonstrated that with an action level of 2 mm, the errors would have been reduced to {Sigma}{sub eff} < 0.6 mm and {sigma}{sub eff} < 0.7 mm, requiring corrections in 82.4% of the fractions. Because iSGT is highly automated, the extra time added by iSGT is <30 s if a correction is required. Conclusions: Without increasing imaging dose, iSGT successfully reduces intrafraction prostate motion with minimal workload and increase in fraction time. An action level of 2 mm is recommended.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Automated beam placement for breast radiotherapy using a support vector machine based algorithm

Purpose: To develop an automated beam placement technique for whole breast radiotherapy using tangential beams. We seek to find optimal parameters for tangential beams to cover the whole ipsilateral breast (WB) and minimize the dose to the organs at risk (OARs). Methods: A support vector machine (SVM) based method is proposed to determine the optimal posterior plane of the tangential beams. Relative significances of including/avoiding the volumes of interests are incorporated into the cost function of the SVM. After finding the optimal 3-D plane that separates the whole breast (WB) and the included clinical target volumes (CTVs) from the OARs, the gantry angle, collimator angle, and posterior jaw size of the tangential beams are derived from the separating plane equation. Dosimetric measures of the treatment plans determined by the automated method are compared with those obtained by applying manual beam placement by the physicians. The method can be further extended to use multileaf collimator (MLC) blocking by optimizing posterior MLC positions. Results: The plans for 36 patients (23 prone- and 13 supine-treated) with left breast cancer were analyzed. Our algorithm reduced the volume of the heart that receives >500 cGy dose (V5) from 2.7 to 1.7 cm{sup 3} (p = 0.058) on average and the volume of the ipsilateral lung that receives >1000 cGy dose (V10) from 55.2 to 40.7 cm{sup 3} (p = 0.0013). The dose coverage as measured by volume receiving >95% of the prescription dose (V95%) of the WB without a 5 mm superficial layer decreases by only 0.74% (p = 0.0002) and the V95% for the tumor bed with 1.5 cm margin remains unchanged. Conclusions: This study has demonstrated the feasibility of using a SVM-based algorithm to determine optimal beam placement without a physician's intervention. The proposed method reduced the dose to OARs, especially for supine treated patients, without any relevant degradation of dose homogeneity and coverage in general.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Automated size-specific CT dose monitoring program: Assessing variability in CT dose

Purpose: The potential health risks associated with low levels of ionizing radiation have created a movement in the radiology community to optimize computed tomography (CT) imaging protocols to use the lowest radiation dose possible without compromising the diagnostic usefulness of the images. Despite efforts to use appropriate and consistent radiation doses, studies suggest that a great deal of variability in radiation dose exists both within and between institutions for CT imaging. In this context, the authors have developed an automated size-specific radiation dose monitoring program for CT and used this program to assess variability in size-adjusted effective dose from CT imaging. Methods: The authors radiation dose monitoring program operates on an independent health insurance portability and accountability act compliant dosimetry server. Digital imaging and communication in medicine routing software is used to isolate dose report screen captures and scout images for all incoming CT studies. Effective dose conversion factors (k-factors) are determined based on the protocol and optical character recognition is used to extract the CT dose index and dose-length product. The patient's thickness is obtained by applying an adaptive thresholding algorithm to the scout images and is used to calculate the size-adjusted effective dose (ED{sub adj}). The radiation dose monitoring program was used to collect data on 6351 CT studies from three scanner models (GE Lightspeed Pro 16, GE Lightspeed VCT, and GE Definition CT750 HD) and two institutions over a one-month period and to analyze the variability in ED{sub adj} between scanner models and across institutions. Results: No significant difference was found between computer measurements of patient thickness and observer measurements (p= 0.17), and the average difference between the two methods was less than 4%. Applying the size correction resulted in ED{sub adj} that differed by up to 44% from effective dose estimates that were not adjusted by patient size. Additionally, considerable differences were noted in ED{sub adj} distributions between scanners, with scanners employing iterative reconstruction exhibiting significantly lower ED{sub adj} (range: 9%-64%). Finally, a significant difference (up to 59%) in ED{sub adj} distributions was observed between institutions, indicating the potential for dose reduction. Conclusions: The authors developed a robust automated size-specific radiation dose monitoring program for CT. Using this program, significant differences in ED{sub adj} were observed between scanner models and across institutions. This new dose monitoring program offers a unique tool for improving quality assurance and standardization both within and across institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Foreign object detection and removal to improve automated analysis of chest radiographs

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-D-16A-02: A Novel Methodology for Accurate, Semi-Automated Delineation of Oral Mucosa for Radiation Therapy Dose-Response Studies

Purpose: The significant morbidity caused by radiation-induced acute oral mucositis means that studies aiming to elucidate dose-response relationships in this tissue are a high priority. However, there is currently no standardized method for delineating the mucosal structures within the oral cavity. This report describes the development of a methodology to delineate the oral mucosa accurately on CT scans in a semi-automated manner. Methods: An oral mucosa atlas for automated segmentation was constructed using the RayStation Atlas-Based Segmentation (ABS) module. A radiation oncologist manually delineated the full surface of the oral mucosa on a planning CT scan of a patient receiving radiotherapy (RT) to the head and neck region. A 3mm fixed annulus was added to incorporate the mucosal wall thickness. This structure was saved as an atlas template. ABS followed by model-based segmentation was performed on four further patients sequentially, adding each patient to the atlas. Manual editing of the automatically segmented structure was performed. A dose comparison between these contours and previously used oral cavity volume contours was performed. Results: The new approach was successful in delineating the mucosa, as assessed by an experienced radiation oncologist, when applied to a new series of patients receiving head and neck RT. Reductions in the mean doses obtained when using the new delineation approach, compared with the previously used technique, were demonstrated for all patients (median: 36.0%, range: 25.6% – 39.6%) and were of a magnitude that might be expected to be clinically significant. Differences in the maximum dose that might reasonably be expected to be clinically significant were observed for two patients. Conclusion: The method developed provides a means of obtaining the dose distribution delivered to the oral mucosa more accurately than has previously been achieved. This will enable the acquisition of high quality dosimetric data for use in dose-response studies. We would like to thank the Engineering and Physical Sciences Research Council for funding. We acknowledge support from the NIHR RM/ICR Biomedical Research Centre. RayStatation was used under an evaluation agreement with RaySearch Laboratories AB.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-I-89: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Pediatric Anthropomorphic and ACR Phantoms

Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-191: Automated Detection of Anatomic Changes in H'N Patients

Purpose: To develop a novel statistics-based method for automated detection of anatomical changes using cone-beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just-in-time” adaptation of the treatment plan. Methods: Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold-based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two-sample Kolmogorov-Smirnov test, which is a non-parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H'N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H'N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection. Results: The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy. Conclusion: The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co-registration facilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-48: Automated Quality Assurance for XML Controlled Linacs

Purpose: To automate routine imaging QA procedures so that complying with TG 142 and TG 179 can be efficient and reliable. Methods: Two QA tests for a True Beam Linac were automatized. A Winston Lutz test as described by Lutz et al{sup 1} using the Winston Lutz test kit from BrainLab, Germany and a CBCT Image Quality test as described in TG 179 using the EMMA phantom, Siemens Medical Physics, Germany were performed in our True Beam. For each QA procedure tested, a 3 step paradigm was used. First, the data was automatically acquired using True Beam Developer Mode and XML scripting. Second, the data acquired in the first step was automatically processed using in-home grown Matlab GUIs. Third, Machine Learning algorithms were used to automatically classify the processed data and reports generated. Results: The Winston Luzt test could be performed by an experienced medical physicist in 29.0 ± 8.0 min. The same test, if automated using our paradigm, could be performed in 3.0 ± 0.1 min. In the same lieu, time could be substantially saved for image quality tests. In this case, the amount of time saved will depend on the phantoms used and the initial localization method. Additionally, machine learning algorithms could automatically identify the roots of the problems if any and possibly help reduce machine down time. Conclusion: Modern linear accelerators are equipped with advanced 2D and 3D imaging that are used for patient alignment substantially improving IGRT protocols. However, this extra complexity exponentially increases the number of QA tests needed. Using the new paradigm described above, not only bare minimum but best practice QA programs could be implemented with the same manpower. This work is supported by Varian, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-88: Comprehensive Automated Daily QA for Hypo- Fractionated Treatments

Purpose: The trend towards more SBRT treatments with fewer high dose fractions places increased importance on daily QA. Patient plan specific QA with 3%/3mm gamma analysis and daily output constancy checks may not be enough to guarantee the level of accuracy required for SBRT treatments. But increasing the already extensive amount of QA procedures that are required is a daunting proposition. We performed a feasibility study for more comprehensive automated daily QA that could improve the diagnostic capabilities of QA without increasing workload. Methods: We performed the study on a Siemens Artiste linear accelerator using the integrated flat panel EPID. We included square fields, a picket fence, overlap and representative IMRT fields to measure output, flatness, symmetry, beam center, and percent difference from the standard. We also imposed a set of machine errors: MLC leaf position, machine output, and beam steering to compare with the standard. Results: Daily output was consistent within +/− 1%. Change in steering current by 1.4% and 2.4% resulted in a 3.2% and 6.3% change in flatness. 1 and 2mm MLC leaf offset errors were visibly obvious in difference plots, but passed a 3%/3mm gamma analysis. A simple test of transmission in a picket fence can catch a leaf offset error of a single leaf by 1mm. The entire morning QA sequence is performed in less than 30 minutes and images are automatically analyzed. Conclusion: Automated QA procedures could be used to provide more comprehensive information about the machine with less time and human involvement. We have also shown that other simple tests are better able to catch MLC leaf position errors than a 3%/3mm gamma analysis commonly used for IMRT and modulated arc treatments. Finally, this information could be used to watch trends of the machine and predict problems before they lead to costly machine downtime.

60 APPLIED LIFE SCIENCES↗