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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Real-Time Digital Simulation of Inertial Response with Hardware-in-the-Loop Implementation on the CART3 Wind Turbine at the National Wind Technology Center

With increasing penetrations of wind power on electric grids, the stability and reliability of interconnected power systems may be impacted. In some countries that have developed renewable energy sources and systems, grid codes have been revised to require wind power plants (WPPs) to provide ancillary services to support the power system frequency in case of severe grid events. To do this, wind turbine generators (WTGs) should be deloaded to reserve a certain amount of active power for primary frequency response; however, deloading curtails annual energy production, and the market for this type of service needs to be further developed. In this report, we focus on the temporary frequency support provided by WTGs through inertial response. WTGs have potential to provide inertial response, but appropriate control methods should be implemented. With the implemented inertial control methods, wind turbines are capable of increasing their active power output by releasing some of their stored kinetic energy when a frequency excursion occurs. Active power can be temporarily boosted above the maximum power points, after which the rotor speed decelerates, and subsequently an active power output reduction restores the kinetic energy. In this report, we develop two types of models for wind power systems: the first is common, based on the wind power aerodynamic equation, and the power coefficient can be regressed using nonlinear functions; the second is much more complicated, wherein the wind turbine system is modeled using the Fatigue, Aerodynamics, Structures, and Turbulence Modeling (FAST) tool with several degrees of freedoms. A nine-bus test power system is built in Simulink and the Real-Time Digital Simulator, respectively, which are used to evaluate the frequency support performance of the WPPs. We implement two distinct types of inertial control methods in the modeled wind turbines: frequency-based inertial control (FBIC) and stepwise inertial control (SIC). We compare the performances of the two methods in terms of their frequency nadirs, rates of change of frequency, and recovery times. We conclude the results under various wind speeds and penetration cases, which provide insight into designing the inertial response of WTGs. Further, we discuss the impact of the parameters on the performance of the inertial control methods. We evaluate both the scaling factors for the FBIC method and the slope values for the TLIC methods. The simulation work shows the characteristics of different inertial responses compared to conventional synchronous generators. Based on the simulation results, we modify, improve, and test the inertial control methods under a more realistic wind turbine model based on FAST. We then validate the inertial responses under highly turbulent wind conditions generated by TurbSim, and we examine their influences on the turbine mechanical components. The extensive simulation proves the effectiveness of the proposed inertial control methods as well as the nine-bus test power system. We then reconsider the parameters. We rebuild the same test power system using Real time Simulator Computer Aided Design (RSCAD), and we implement the inertial control methods in the real Controls Advanced Research Turbine (CART3), which is prepared for the hardware-in-the-loop field-test simulation. After the setups for the hardware and software hybrid simulation platform are complete, the inertial response is further tested on a real wind turbine for the first time, in which CART3 release the controlled inertial response against the emulated frequency excursion, provided by the real-time simulated power system test bed in RTDS.

17 WIND ENERGY↗
DOE OSTI2019

Assessing the Need for Preprocedural Laboratory Tests and Stopping Non-steroidal Anti-inflammatory Drugs/Aspirin in Patients Undergoing Percutaneous Bone and Soft Tissue Biopsies

Purpose: Although image-guided biopsies of bone and soft tissue lesions have a low complication rate, there is limited data evaluating use of preprocedural laboratory tests. To address this issue, patients were not required to stop non-steroidal antiinflammatory drugs (NSAIDs) and aspirin or to obtain preprocedural laboratory tests [complete blood count (CBC) and international normalized ratio (INR)], except in special circumstances. The bleeding complication rate, rate of same day biopsies, and the time from when the biopsy was ordered to when it was performed were obtained. Materials and Methods: A total of 332 patients who underwent bone or soft tissue biopsies performed at our institution between 9/1/2017 and 1/9/2019 were prospectively analyzed. These data were compared to a retrospective biopsy cohort of 323 patients between 7/1/2015 and 7/1/2017. Data collected included method of image guidance and bleeding complication rate. The number of days from ordering to performing a biopsy and number of same day biopsies were recorded. Results: There were no bleeding complications in either cohort (OR 1.00, P = 1). The mean time from ordering to performing a bone biopsy was significantly decreased in the prospective group (6.6 days) compared to the retrospective group (8.1 days) (P = 0.012). There were more same day biopsies in the prospective cohort (11.4% vs. 3.4%) (P < 0.001). Conclusions: Preprocedural CBC and INR for bone and soft tissue biopsies can be safely eliminated in most patients. Biopsies performed while patients are taking NSAIDs/aspirin can safely be performed. Adopting revised preprocedural laboratory criteria can result in decreased time to completion of biopsies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2015

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is supported by NIH/NIBIB (1R01-EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Realistic fuel spray modeling for gasoline direct injection engine applications

Fuel spray modeling plays a critical role during modern gasoline direct injection (GDI) engine development due to fuel injection’s dominant impact on engine performance and emissions as well as the complex physical processes involved. In engineering three-dimensional (3D) computational fluid dynamics (CFD) simulations, the liquid-phase fuel atomization, evaporation, and mixing are usually modeled with the discrete droplet model (DDM) adopting a Lagrangian approach for multiphase CFD simulations. To this end, general practices heavily depend on the reduced order characterization of the injector nozzle flow. However, such simplified injector modeling may lead to insufficient representations of the complex spray dynamics. To tackle this problem, this study proposes a novel workflow to numerically evaluate GDI sub-cooled and flash-boiling sprays under engine-relevant conditions using a side-mounted GDI injector together with real gasoline fuel properties. The workflow introduces a one-way coupling (OWC) method leveraging high-fidelity nozzle flow simulations to provide realistic boundary conditions to the Lagrangian injector model. The proposed workflow was first verified in a constant volume chamber (CVC) environment and then implemented in a practical GDI engine setup to study spray morphology, fuel-air mixing, and wall-wetting propensity. In addition, detailed comparison was performed between the OWC method and the conventional rate of injection (ROI) routine. In conclusion, quantitative analysis of spray characteristics was conducted to highlight possible source of discrepancies of the conventional ROI method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Modification of a Silicon Photomultiplier for Reduced High Temperature Dark Count Rate

In this work we present a novel method for improving the high-temperature performance of silicon photomultipliers (SiPMs) via focused ion beam (FIB) modification of individual microcells. The literature suggests that most of the dark count rate (DCR) in a SiPM is contributed by a small percentage (<5%) of microcells. By using a FIB to electrically deactivate this relatively small number of microcells, we believe we can greatly reduce the overall DCR of the SiPM at the expense of a small reduction in overall photodetection efficiency, thereby improving its high temperature performance. In this report we describe our methods for characterizing the SiPM to determine which individual microcells contribute the most to the DCR, preparing the SiPM for FIB, and modifying the SiPM using the FIB to deactivate the identified microcells.

42 ENGINEERING↗
DOE OSTI2024

Development of algorithms for augmenting and replacing conventional process control using reinforcement learning

Here, this work seeks to allow for the online operation and training of model-free reinforcement learning (RL) agents but limit the risk to system equipment and personnel. The parallel implementation of RL alongside more conventional process control (CPC) allows for the RL algorithm to learn from CPC. The past performance of both methods are assessed on a continuous basis allowing for a transition from CPC to RL and, if needed, transitioning back to CPC from RL. This allows for the RL algorithm to slowly and safely assume control of the process without significant degradation in control performance. It is shown that the RL can derive a near optimal policy even when coupled with a suboptimal CPC. It is also demonstrated that the coupled RL-CPC algorithm learns at a faster rate than traditional RL methods of exploration while the algorithm’s performance does not deteriorate below CPC, even when exposed to an unknown operating condition.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

SU-E-T-115: Analysis of Patient Specific QA for VMAT by Disease Site and Planning-Delivery System Using the ScandiDos Delta4 Phantom

Purpose: To evaluate patient specific quality assurance (PSQA) for the delivery of volumetric modulated arc therapy (VMAT) by disease site. To compare planning-delivery system (PDS) PSQA pass rates in a dual vendor institution. Methods: PSQA is performed for VMAT plans using a ScandiDos Delta4 phantom. Verification plans are calculated using Varian Eclipse and Elekta Monaco treatment planning systems (TPS) for patients treated using Varian Truebeam and Elekta linear accelerators respectively. Individual arcs are delivered to the Delta4 phantoms and assessed using the gamma index pass criterion(3% Dose Deviation(DD%), 3mm Distance to Agreement(DTA),10% dose threshold and 90% gamma index). Results: A total of 287 VMAT plans and 680 arcs were analyzed. The passing rates for VMAT QA plans were 95% and 98% for head/neck and pelvis/prostate plans respectively, and 100% for chest/abdomen, spine, lung Stereotactic Body Radiotherapy (SBRT) and Stereotactic Radiosurgery(SRS) plans. Average gamma indices were: (99 ± 2) % for pelvis/prostate, chest/abdomen and lung SBRT plans, (97 ± 4) % for head and neck plans and (98 ± 3) % for spine plans. The average DD% and DTA pass rates ranged from 82% to 90% and 98% to 99% respectively for plans in different disease sites. Paired t-test analysis (two tails) indicated no significant differences in the gamma indices between plans delivered using different PDS; the P values were: 0.08, 0.45, and 0.94 for lung SBRT, head/neck and pelvis/prostate plans respectively. The statistical power for comparing PDS in different disease sites with an alpha of 0.05 is 1. Conclusion: The Gamma indices based on 3% DD%, 3 mm DTA and 10% dose threshold for the VMAT QA plans in all disease sites were well above 90%, suggesting the possibility of using a more stringent PSQA criterion. No significant differences were observed in the QA of VMAT plans delivered using different PDS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A New Estimator to Correct for Bias from Tag Rate Expansion on Natural-Origin Fish Attributes in Mixed-Stock Analysis Using Parentage-Based Tagging

Abstract In fisheries where hatchery- and natural-origin conspecifics occur as mixed stocks, it is often important to estimate both the natural-origin proportion of the mixture and the composition of attributes within the natural-origin portion (e.g., genetic stock, sex, and age-class). These estimates are facilitated by parentage-based tagging, which allows large numbers of hatchery fish to be efficiently tagged and later identified. When tag rates are less than 100%, the untagged fish in the mixture include both untagged hatchery fish and natural-origin fish. Unbiased estimation of the abundance and attribute composition of the natural-origin portion requires an estimator that accounts for tag rates. Two estimators are described: one “accounting-style” estimator similar to previously described approaches and one maximum likelihood method. These estimators were evaluated and compared using simulations mimicking estimation of the composition of fish migrating past a dam. The two estimators performed similarly at reducing bias when tag rates were high, but the maximum likelihood method had smaller mean square error when tag rates were low. We provide an R package to allow usage of this estimator in a wide variety of fisheries applications.

Delomas, Thomas A. (ORCID:000000015154759X)↗
DOE OSTI2024

Experimental Analysis of Distribution Network Voltage Regulation Using Smart Inverters

Smart inverters (SIs) have demonstrated their potential to provide grid services for both transmission and distribution systems. One of these grid services, distribution network voltage regulation by SIs, has the potential to improve network voltage regulation through controlling the reactive and active power output of the SIs. Voltage regulation by SIs will be distributed and might be better suited to controlling local conditions to complement traditional voltage-regulating assets, e.g., tap-changing transformers, capacitor banks, and line voltage regulators. There is a gap in the literature on comparing the SI response characteristics when the SIs are controlled by a local controller or external control signals. This paper presents an experimental study to characterize SI reactive power regulation responses to two different control methods: autonomous control and remote dispatch. We found that SI reactive power regulation responses exhibit important differences between these methods in terms of delays and ramp rate. Finally, power-hardware-in-the-loop (PHIL) tests were conducted to evaluate the performance of these two methods. The PHIL test results show that the SI response characteristics for autonomous control and remote dispatch need to be considered when planning for distribution network voltage regulation using SIs.

autonomous control↗
DOE OSTI2020

Comparing Cetane Number Measurement Methods

Cetane number is one of the most important fuel performance metrics for mixing controlled compression-ignition “diesel” engines, quantifying a fuel’s propensity for autoignition when injected into end-of-compression-type temperature and pressure conditions. The historical default and referee method on a Cooperative Fuel Research (CFR) engine configured with indirect fuel injection and variable compression ratio is cetane number (CN) rating. A subject fuel is evaluated against primary reference fuel blends, with heptamethylnonane defining a low-reactivity endpoint of CN = 15 and hexadecane defining a high-reactivity endpoint of CN = 100. While the CN scale covers the range from zero (0) to 100, typical testing is in the range of 30 to 65 CN. Alternatively, several constant-volume combustion chamber (CVCC)-based cetane rating devices have been developed to rate fuels with an equivalent derived cetane number (DCN) or indicated cetane number (ICN). These devices measure ignition delay for fuel injected into a fixed volume of high-temperature and high-pressure air to simulate end-of-compression-type conditions. In this study, a range of novel fuel compounds are evaluated across three CVCC methods: the Ignition Quality Tester (IQT), Fuel Ignition Tester (FIT), and Advanced Fuel Ignition Delay Analyzer (AFIDA). Resulting DCNs and ICNs are compared for fuels within the normal diesel fuel range of reactivity, as well as very high (~100) and very low DCNs/ICNs (~5). Distinct differences between results from various devices are discussed. This is important to consider because some new, high-efficiency advanced compression-ignition (CI) engine combustion strategies operate with more kinetically controlled distributed combustion as opposed to mixing controlled diffusion flames. These advanced combustion strategies may benefit from new fuel chemistries, but current rating methods of CN, DCN, and ICN may not fully describe their performance. In addition, recent evidence suggests ignition delay in modern on-road diesel engines with high-pressure common rail fuel injection systems may no longer directly correlate to traditional CN fuel ratings. Simulated end-of-compression conditions are compared for CN, DCN, and ICN and discussed in the context of modern diesel engines to provide additional insight. Results highlight the potential need for revised and/or multiple fuel test conditions to measure fuel performance for advanced CI strategies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

A Kriging based spatiotemporal approach for traffic volume data imputation

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.

Science & Technology - Other Topics↗
DOE OSTI2015

Define and Quantify the Physics of Air Flow, Pressure Drop and Aerosol Collection in Nuclear Grade HEPA Filters

Objective: Develop a set of peer-review and verified analytical methods to adjust HEPA filter performance to different flow rates, temperatures and altitudes. Experimental testing will measure HEPA filter flow rate, pressure drop and efficiency to verify the analytical approach. Nuclear facilities utilize HEPA (High Efficiency Particulate Air) filters to purify air flow for workspace ventilation. However, the ASME AG-1 technical standard (Code on Nuclear Air and Gas Treatment) does not adequately describe air flow measurement units for HEPA filter systems. Specifically, the AG-1 standard does not differentiate between volumetric air flow in ACFM (actual cubic feet per minute)compared to mass flow measured in SCFM (standard cubic feet per minute). More importantly, the AG-1 standard has an overall deficiency for using HEPA filter devices at different air flow rates, temperatures, and altitudes. Technical Approach: The collection efficiency and pressure drops of 18 different HEPA filters will be measured over a range of flow rates, temperatures and altitudes. The experimental results will be compared to analytical scoping calculations. Three manufacturers have allocated six HEPA filters each for this effort. The 18 filters will be tested at two different flow rates, two different temperatures and two different altitudes. The 36 total tests will be conducted at two different facilities: the ATI Test facilities (Baltimore MD) and the Los Alamos National Laboratory (Los Alamos NM). The Radiation Protection RP-SVS group at Los Alamos has an aerosol wind tunnel that was originally designed to evaluate small air samplers. In 2010, modifications were started to convert the wind tunnel for HEPA filter testing. (Extensive changes were necessary for the required aerosol generators, HEPA test fixtures, temperature control devices and measurement capabilities.) To this date, none of these modification activities have been funded through a specific DOE or NNSA program. This is expected to require six months of time, after receipt of funding. Benefits: US DOE facilities that use HEPA filters will benefit from access to the new operational measurement methods. Uncertainty and guesswork will be removed from HEPA filter operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Virtual Guidance of Percutaneous Transthoracic Needle Biopsy with C-Arm Cone-Beam CT: Diagnostic Accuracy, Risk Factors and Effective Radiation Dose

Purpose: C-arm cone-beam computed tomography-guided transthoracic lung core needle biopsy (CBCT-CNB) is a safe and accurate procedure for the evaluation of patients with pulmonary nodules. The purpose of our study was to evaluate the diagnostic performance, complication rates and effective radiation dose of CBCT-CNB with virtual guidance. Materials and Methods: We retrospectively collected data regarding 375 CBCT-CNBs performed with virtual guidance (XperGuide—Philips Healthcare, Best, The Netherlands) from January 2010 to June 2015 on 355 patients (mean age, 68.1 years ± 11.8; age range, 31–88 years). Patients were divided into groups and compared based on the diagnostic failure and lesion size (15 mm cutoff). Diagnostic performance, complication rate and effective radiation dose were investigated. Variables influencing diagnostic performance and complications were assessed using Student’s T test and Pearson’s χ{sup 2} test. Results: The sensitivity, specificity, positive and negative predictive value and accuracy for patients subjected to CNBs were 96.8%, 100%, 100%, 100% and 97.2%, respectively. Considering risk factors for pneumothorax, no significant differences were found regarding patient and lesion characteristics. Perilesional hemorrhage occurred more frequently in older patients (p = 0.046) and in smaller lesions (p = 0.001). Hemoptysis was significantly more frequent in patients with perilesional hemorrhage (p = 0.01). Mean effective radiation dose in CBCT-CNB was 7.12 ± 8.78 mSv. Conclusions: CBCT-CNB combined with virtual guidance is a reliable and accurate technique that allows exact localization of pulmonary lesions, effective preprocedural planning and real-time fluoroscopy altogether.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

TU-H-CAMPUS-JeP1-02: Fully Automatic Verification of Automatically Contoured Normal Tissues in the Head and Neck

Purpose: To investigate and validate the use of an independent deformable-based contouring algorithm for automatic verification of auto-contoured structures in the head and neck towards fully automated treatment planning. Methods: Two independent automatic contouring algorithms [(1) Eclipse’s Smart Segmentation followed by pixel-wise majority voting, (2) an in-house multi-atlas based method] were used to create contours of 6 normal structures of 10 head-and-neck patients. After rating by a radiation oncologist, the higher performing algorithm was selected as the primary contouring method, the other used for automatic verification of the primary. To determine the ability of the verification algorithm to detect incorrect contours, contours from the primary method were shifted from 0.5 to 2cm. Using a logit model the structure-specific minimum detectable shift was identified. The models were then applied to a set of twenty different patients and the sensitivity and specificity of the models verified. Results: Per physician rating, the multi-atlas method (4.8/5 point scale, with 3 rated as generally acceptable for planning purposes) was selected as primary and the Eclipse-based method (3.5/5) for verification. Mean distance to agreement and true positive rate were selected as covariates in an optimized logit model. These models, when applied to a group of twenty different patients, indicated that shifts could be detected at 0.5cm (brain), 0.75cm (mandible, cord), 1cm (brainstem, cochlea), or 1.25cm (parotid), with sensitivity and specificity greater than 0.95. If sensitivity and specificity constraints are reduced to 0.9, detectable shifts of mandible and brainstem were reduced by 0.25cm. These shifts represent additional safety margins which might be considered if auto-contours are used for automatic treatment planning without physician review. Conclusion: Automatically contoured structures can be automatically verified. This fully automated process could be used to flag auto-contours for special review or used with safety margins in a fully automatic treatment planning system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Motion tracking in the liver: Validation of a method based on 4D ultrasound using a nonrigid registration technique

Purpose: Treatments like radiotherapy and focused ultrasound in the abdomen require accurate motion tracking, in order to optimize dosage delivery to the target and minimize damage to critical structures and healthy tissues around the target. 4D ultrasound is a promising modality for motion tracking during such treatments. In this study, the authors evaluate the accuracy of motion tracking in the liver based on deformable registration of 4D ultrasound images. Methods: The offline analysis was performed using a nonrigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D image data sequence in a groupwise optimization fashion, thus avoiding a bias toward a specifically chosen reference time point. Three healthy volunteers were scanned over several breathing cycles (12 s) from three different positions and angles on the abdomen; a total of nine 4D scans for the three volunteers. Well-defined anatomic landmarks were manually annotated in all 96 time frames for assessment of the automatic algorithm. The error of the automatic motion estimation method was compared with interobserver variability. The authors also performed experiments to investigate the influence of parameters defining the deformation field flexibility and evaluated how well the method performed with a lower temporal resolution in order to establish the minimum frame rate required for accurate motion estimation. Results: The registration method estimated liver motion with an error of 1 mm (75% percentile over all datasets), which was lower than the interobserver variability of 1.4 mm. The results were only slightly dependent on the degrees of freedom of the deformation model. The registration error increased to 2.8 mm with an eight times lower temporal resolution. Conclusions: The authors conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data. The authors believe that the method has potential in interventions on moving abdominal organs such as MR or ultrasound guided focused ultrasound therapy and radiotherapy, pending the method is enabled to run in real-time. The data and the annotations used for this study are made publicly available for those who would like to test other methods on 4D liver ultrasound data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRA-16: Development of a Radiation Monitoring Device Using a Low-Cost CCD Camera Following Radionuclide Therapy

Purpose: It is now commonplace to handle treatments of hyperthyroidism using iodine-131 as an outpatient procedure due to lower costs and less stringent federal regulations. The Nuclear Regulatory Commission has currently updated release guidelines for these procedures, but there is still a large uncertainty in the dose to the public. Current guidelines to minimize dose to the public require patients to remain isolated after treatment. The purpose of this study was to use a low-cost common device, such as a cell phone, to estimate exposure emitted from a patient to the general public. Methods: Measurements were performed using an Apple iPhone 3GS and a Cs-137 irradiator. The charge-coupled device (CCD) camera on the phone was irradiated to exposure rates ranging from 0.1 mR/hr to 100 mR/hr and 30-sec videos were taken during irradiation with the camera lens covered by electrical tape. Interactions were detected as white pixels on a black background in each video. Both single threshold (ST) and colony counting (CC) methods were performed using MATLAB®. Calibration curves were determined by comparing the total pixel intensity output from each method to the known exposure rate. Results: The calibration curve showed a linear relationship above 5 mR/hr for both analysis techniques. The number of events counted per unit exposure rate within the linear region was 19.5 ± 0.7 events/mR and 8.9 ± 0.4 events/mR for the ST and CC methods respectively. Conclusion: Two algorithms were developed and show a linear relationship between photons detected by a CCD camera and low exposure rates, in the range of 5 mR/hr to 100-mR/hr. Future work aims to refine this model by investigating the dose-rate and energy dependencies of the camera response. This algorithm allows for quantitative monitoring of exposure from patients treated with iodine-131 using a simple device outside of the hospital.

60 APPLIED LIFE SCIENCES↗