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At least 325 records · Page 18

SU-F-P-54: Guidelines to Check Image Registration QA of a Clinical Deformation Registration Software: A Single Institution Preliminary Study

Purpose: The objective of this study is to verify and analyze the accuracy of a clinical deformable image registration (DIR) software. Methods: To test clinical DIR software qualitatively and quantitatively, we focused on lung radiotherapy and analyzed a single (Lung) patient CT scan. Artificial anatomical changes were applied to account for daily variations during the course of treatment including the planning target volume (PTV) and organs at risk (OAR). The primary CT (pCT) and the structure set (pST) was deformed with commercial tool (ImSimQA-Oncology Systems Limited) and after artificial deformation (dCT and dST) sent to another commercial tool (VelocityAI-Varian Medical Systems). In Velocity, the deformed CT and structures (dCT and dST) were inversely deformed back to original primary CT (dbpCT and dbpST). We compared the dbpST and pST structure sets using similarity metrics. Furthermore, a binary deformation field vector (BDF) was created and sent to ImSimQA software for comparison with known “ground truth” deformation vector fields (DVF). Results: An image similarity comparison was made by using “ground truth” DVF and “deformed output” BDF with an output of normalized “cross correlation (CC)” and “mutual information (MI)” in ImSimQA software. Results for the lung case were MI=0.66 and CC=0.99. The artificial structure deformation in both pST and dbpST was analyzed using DICE coefficient, mean distance to conformity (MDC) and deformation field error volume histogram (DFEVH) by comparing them before and after inverse deformation. We have noticed inadequate structure match for CTV, ITV and PTV due to close proximity of heart and overall affected by lung expansion. Conclusion: We have seen similarity between pCT and dbpCT but not so well between pST and dbpST, because of inadequate structure deformation in clinical DIR system. This system based quality assurance test will prepare us for adopting the guidelines of upcoming AAPM task group 132 protocol.

60 APPLIED LIFE SCIENCES↗

SU-F-I-02: Comparative Analysis and Constancy Check of Image Quality Parameters for Three Linear Accelerators Per TG 142 Protocol

Purpose: To compare image quality parameters and assessing the image stability of three different linear accelerators (linac) for 2D and 3D imaging modalities: planar kV, MV images and cone-beam CT (CBCT). Methods: QCkV1, QC-3 and Cathpan-600 phantoms were utilized to acquire kV, MV and CBCT images respectively on monthly basis per TG142 QA protocol for over 2 years on 21Ex, NovalisTx and TrueBeam linacs. DICOM images were analyzed with the help of QA analysis software: PIPsPro from Standard Imaging. For planar kV and MV images, planar spatial resolution, contrast to noise ratio (CNR) and noise; for CBCT, HU values were collected and analyzed. Results: Two years of monthly QA measurements were analyzed for the planar and CBCT images. Values were normalized to the mean and the standard deviations (STD) are presented. For the kV planar radiographic images the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.006, 0.011, 0.013, 0.046, 0.026; Novalis-Tx are 0.009, 0.016, 0.016, 0.067, 0.053 ; TrueBeam are 0.007, 0.005, 0.009, 0.017, 0.016 respectively. For the MV planar radiographic images, the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.009, 0.010, 0.008, 0.023, 0.023; for Novalix-Tx are 0.012, 0.010, 0.008, 0.029, 0.023 and for TrueBeam are 0.010, 0.010, 0.007, 0.022, 0.022 respectively. For the CBCT images, HU constancies of Air, Polystyrene, Teflon, PMP, LDPE and Delrin for 21Ex are 0.014, 0.070, 0.031, 0.053, 0.076, 0.087; for Novalis Tx are 0.019, 0.047, 0.035, 0.059, 0.077, 0.087 and for TrueBeam are 0.011, 0.044, 0.025, 0.044, 0.056, 0.020 respectively. Conclusion: These Imaging QA results demonstrated that the TrueBeam, performed better in terms of image quality stability for both kV planer and CBCT images as well as EPID MV images, however other two linacs were also satisfied TG142 guidelines.

60 APPLIED LIFE SCIENCES↗

When to vaccinate for seasonal influenza: check the peak forecast

Background Seasonal influenza infects 5-20% of people every year in the United States, resulting in hospitalizations, deaths, and adverse economic impacts. To mitigate these impacts, influenza vaccines are developed and distributed annually; however, growing evidence suggests that vaccine effectiveness (VE) wanes over the course of a flu season. Delaying influenza vaccination for older adults has attracted attention as a potential public health strategy. However, given the uncertainties in seasonal peak, vaccine effectiveness, and waning rates, postponing vaccination could also lead to increased morbidity, motivating an evaluation of a range of potential scenarios. The aim of this study was to investigate favorable age group-specific vaccination schedules that could lead to the greatest disease burden reduction. Methods We systematically investigated a broad range of vaccination start times for five age groups under six combinations of initial effectiveness and waning rates, based on influenza cases and vaccine uptake data from 10 influenza seasons. We defined the most favorable vaccination schedule as the one that resulted in the greatest reduction in disease burden. Results In scenarios with fast waning, all age groups benefit from delaying vaccination regardless of initial VE and peak timing. In scenarios with slower waning, results are mixed. For the ≥65 group, high initial VE and slow waning suggests that in early-peaking seasons, early vaccination most effectively reduces disease burden, while in late-peaking seasons delaying vaccination is most effective. For the ≥65 group in medium and low initial VE, and slow waning scenarios, delaying vaccination appears to prevent the greatest number of cases, regardless of whether the season peaks early or late. Conclusion The most favorable vaccination schedule is sensitive to changes in initial VE, waning rate, and peak timing. Given estimates of these quantities from statistical and immunological models and observations, our methods can inform vaccination recommendations in order to most effectively reduce the annual disease burden caused by seasonal influenza. Specifically, accurate peak timing forecasts for the upcoming season have the potential to guide decisions on when to vaccinate.

59 BASIC BIOLOGICAL SCIENCES↗

Continuous-Energy Data Checks

The purpose of this report is to provide an overview of all Quality Assurance tests that have to be performed on a nuclear data set to be transformed into an ACE formatted nuclear data file. The ACE file is capable of containing different types of data such as continuous energy neutron data, thermal scattering data, etc. Within this report, we will limit ourselves to continuous energy neutron data.

99 GENERAL AND MISCELLANEOUS↗

Check the Stack: An Enabling Framework for Resilient Microgrids

Highly destructive natural disasters have illustrated the fragility of critical infrastructure including emergency services, healthcare, and water treatment during long-term electricity outages. One pathway to increase the resilience of critical infrastructure amidst disruptions to grid systems is developing microgrids, particularly those that incorporate solar photovoltaics (PV) and energy storage. This document defines a resilient microgrid as a set of interconnected load that includes critical infrastructure and distributed energy resources with the ability to island from the grid during an outage. Research is limited with respect to how much critical infrastructure could benefit from microgrids, how many states have pursued this prioritization approach, and what policies need to be in place to support more widespread deployment. This report explores each of these questions to offer the industry and policymakers a clearer understanding of the opportunity for resilient microgrids and how policy can be designed to support this market.

14 SOLAR ENERGY↗

Results of the Analyses of SMECT Mercury Sample Collected During Mercury Pump (MB1) Functional Check

A pump added to remove mercury from the sump of the Slurry Mix Evaporator Condensate Tank (SMECT) in DWPF was successfully tested in 2020, resulting in a sample of contaminated mercury obtained from the Mercury Purification Process (MPP) cell. The mercury in the sump of the SMECT was present as a result of steam stripping of sludge slurry in the Sludge Receipt and Adjustment Tank (SRAT) at DWPF. A schematic of the MPP system provided by DWPF personnel is shown in Appendix A. DWPF personnel supplied the following detailed description of the Hg sampling: “The sample provided is the result of DWPF extraction of mercury by means of the Mercury Pump-Water Cart. The Mercury Pump extracted the sample from the SMECT mercury sump. The pump uses pressurized water to transfer a small amount of mercury to a leachate bucket located at the Lab Mercury cell. The mercury and water were allowed to be self separated, and mercury was gravity decanted from the leachate bucket.” DWPF personnel indicate that the leachate bucket is equipped with both a top stopcock/drain and a lower/bottom stopcock/drain from which the mercury was decanted or drained out, while the separated water layer remained above the mercury during draining. The DWPF customer requested SRNL to analyze the sample for the following: gross alpha/nonvolatile beta analysis and perform a Toxicity Characteristic Leaching Procedure (TCLP) per the Environmental Protection Agency's Test Methods for Evaluating Solid Waste: Physical/Chemical Methods, SW-846. While the TCLP was preferred, a Total Metals analysis of the eight Resource Conservation and Recovery Act (RCRA) metals was deemed acceptable.2 The analytical data collected on this mercury sample will be used in planning for sample disposition as well to start a profile for future Hg disposition of Hg collected from steam stripping.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

EQ-SANS Detector Distance Check with Laser Alignment Method

In Small-Angle Scattering experiments, accurate distance information between sample and the detector is important. During the winter outage, January 2021, positions of detector, especially along the beam direction, were measured with the laser alignment tool to confirm values regularly deduced from silver behenate measurements. The results show that the current detector-z position of the EQ-SANS is accurate at all typically used detector positions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Secure System Composition and Type Checking using Cryptographic Proofs [Slides]

By using zkSNARKs to prove that values have specific dependent types, it is possible to provably assure compatibility and correctness without revealing sensitive information and extend our trusted computing base well beyond our own system. The approach we developed expands the scope of what non-interactive zero-knowledge proofs can capture to include properties about both the execution and correctness of programs.

97 MATHEMATICS AND COMPUTING↗

Check the Storage Stack: Comparing Behind-the-Meter Energy Storage State Policy Stacks in the United States

A variety of studies and disparate datasets track state energy storage polices, but these datasets do not cover all BTM-related storage policy. Moreover, these databases do not align these policies with the policy stacking framework. Thus, it is unclear what BTM storage policies are adopted across the country, what should comprise a complete storage policy framework or stack, and how states policies compare against that stack. This report addresses this gap in the literature by developing a state policy stack for BTM battery storage that we compare across all fifty states. This first-of-its-kind BTM storage policy stack includes 11 parent policy categories, and 31 associated policies divided across the market preparation, creation, and expansion policy components.

25 ENERGY STORAGE↗

Technical Track on Biomass Carbon Removal and Storage (BiCRS): Mapping bioresources, phase 1 - Consistency check comparing Mission Innovation’s Data Visualization Tool for Bioresources and the Clean Energy Ministerial Biofuture Initiative Global Biomass data accessible via the US Department of Energy’s Bioenergy Knowledge Discovery Framework (KDF)

The Mission Innovation (MI) Carbon Dioxide Removal (CDR) Mission, Technical Track on Biomass Carbon Dioxide Removal and Storage (BiCRS), has produced a biomass resource database for its members. In parallel, Oak Ridge National Laboratory (ORNL) developed the International Feedstock Reporting data portal—herein referred to as the CEM Biofuture-KDF data—on behalf of the Clean Energy Ministerial Biofuture Initiative (CEM Biofuture), as a specific task under Biofuture’s 2024–25 Action Plan. This work was conducted at the request of CEM Biofuture and funded by the U.S. Department of Energy in support of that initiative, and it is hosted within DOE’s Knowledge Discovery Framework (KDF).

09 BIOMASS FUELS↗