Results for “Data Digitization” — Page 15

DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗

DOE OSTI2019

Recovery and calibration of legacy analog data from the Leo Brady Seismic Network for the Source Physics Experiment

The Leo Brady Seismic Network (LBSN) was established in 1960 by Sandia National Laboratories for monitoring underground nuclear tests (UGTs) at the Nevada Test Site— renamed in 2010 to the Nevada National Security Site (NNSS). The LBSN has been in various configurations throughout its existence, but it has been generally comprised of four to six stations at regional distances from the NNSS with evenly spaced azimuthal coverage. Between 1962 and the early 1980s, the LBSN—and a sister network operated by Lawrence Livermore National Laboratory—were the most comprehensive U.S. source of regional seismic data of UGTs. During the pre-digital era, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to the NTS and recorded in analog on hi-fi 8-track AMPEX tapes. These tapes have been stored in temperature-stable buildings or bunkers on the NNSS and Kirtland Air Force Base in Albuquerque, NM for decades and contain the sole record of this irreplaceable data from the analog era; full waveforms of UGTs during this time were never routinely converted to digital form. We have been developing a process over the past few years to recover and calibrate data from these tapes, converting them from FM audio to digital waveforms in ground motion units. The calibration of legacy data from the LBSN is still ongoing. To date, we have digitized tapes from 592 separate UGTs. As a proof-of-concept, we calibrated data from the BOXCAR event.

58 GEOSCIENCES↗

DOE OSTI2022

Researcher Views on Water Data Accessibility, Usability, and Dissemination

Ultimately, the challenges that researchers face with finding, accessing, using, and disseminating water data affect their ability to do mission critical research for DOE and other sponsors. We interviewed PNNL researchers who support DOE’s Water Power Technologies Office and summarize their views on issues pertaining to data access, use, and dissemination. We also take a closer look at approaches to disseminating data to help inform WPTO and lab researcher decisions about incorporating data dissemination into future projects.

13 HYDRO ENERGY↗

DOE OSTI2024

Cosimulating Integrated Energy Systems With Heterogeneous Digital Twins: Matching a Connected World

Energy system integration promises in-creased resiliency and the unlocking of synergies, while also contributing to our goal of decarbonization. It is enabled by both old and new technologies, glued together with data and digital services. Hydrolyzers, heat pumps, distributed renewable generation, smart buildings, and the digital grid edge are all currently the subject of integration with the power system and the energy sector at large. To plan and operate such a multidisciplinary and multisectoral system properly, insight, tools, and expertise are all needed. Further, this is exactly where the state of the art fails to deliver: tools for integrated energy systems (IESs) are still in their infancy, and many times, even academia treats these sectors separately, producing experts in each of them but not across.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

1 ns time to digital converters for the KM3NeT data readout system

The KM3NeT collaboration aims at the construction of a multi-km3 high-energy neutrino telescope in the Mediterranean Sea consisting of thousands of glass spheres (nodes), each of them containing 31 photomultiplier (PMT) of small photocathode area. The readout and data acquisition system of KM3NeT has to collect, treat and send to shore, in an economic way, the enormous amount of data produced by the photomultipliers. For this purpose, 31 high-resolution time-interval measuring channels are implemented on the Field-Programmable Gate Arrays (FPGA) based on Time to Digital Converter (TDC). TDC are very common devices in particles physics experiments. Architectures with low resources occupancy are desirable allowing the implementation of other instrumentation, communication and synchronization systems on the same device. The required resolution to measure both, time of flight and timestamp must be 1 ns. A 4-Oversampling technique with two high frequency clocks is used to achieve this resolution. The proposed TDC firmware is developed using very few resources in Xilinx Kintex-7.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

EVIDENCE FOR PHOTOIONIZATION-DRIVEN BROAD ABSORPTION LINE VARIABILITY

We present a qualitative analysis of the variability of quasar broad absorption lines using the large multi-epoch spectroscopic data set of the Sloan Digital Sky Survey Data Release 10. We confirm that variations of absorption lines are highly coordinated among different components of the same ion or the same absorption component of different ions for C iv, Si iv, and N v. Furthermore, we show that the equivalent widths (EWs) of the lines decrease or increase statistically when the continuum brightens or dims. This is further supported by the synchronized variations of emission and absorption-line EWs when the well-established intrinsic Baldwin effect for emission lines is taken into account. We find that the emergence of an absorption component is usually accompanied by the dimming of the continuum while the disappearance of an absorption-line component is accompanied by the brightening of the continuum. This suggests that the emergence or disappearance of a C iv absorption component is only the extreme case, when the ionic column density is very sensitive to continuum variations or the continuum variability the amplitude is larger. These results support the idea that absorption-line variability is driven mainly by changes in the gas ionization in response to continuum variations, that the line-absorbing gas is highly ionized, and in some extreme cases, too highly ionized to be detected in UV absorption lines. Due to uncertainties in the spectroscopic flux calibration, we cannot quantify the fraction of quasars with asynchronized continuum and absorption-line variations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Far-ultraviolet to FIR Spectral-energy Distribution Modeling of the Stellar Formation History of the M31 Bulge

M31 is being surveyed at far- and near-ultraviolet with the UVIT telescope on AstroSat. The central bulge of M31 was observed in the N279N (275–280 nm), N219M (200–240 nm), F172M (160–185 nm), F169M (145–175 nm), and F148W (120–180 nm) filters. These images are made publicly available here. The UVIT data are supplemented with Sloan Digital Sky Survey data in optical, Spitzer data in near-infrared and Herschel data in mid- and far-infrared. The resulting far-ultraviolet to IR spectral-energy distributions for the bulge and for 10 subregions, are modeled using combinations of simple stellar populations and with CIGALE models. We find a dominant old (10–12 Gyr) metal-rich ([Z/H] ~ 0.3) population and a younger (600 Myr) solar abundance ([Z/H] ~ 0) population throughout the bulge. For the innermost 120" we find an additional very young (25 Myr) metal-poor ([Z/H] ~ –0.7) population. The results are consistent with the most recent stellar population studies of the bulge, which find the two populations for the whole bulge and a third young population in the innermost bulge.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

The Global Structure of the Milky Way’s Stellar Halo Based on the Orbits of Local Metal-poor Stars

We analyze the global structure of the Milky Way (MW)'s stellar halo, including its dominant subcomponent, Gaia-Sausage-Enceladus (GSE). The method for reconstructing the global distribution of this old stellar component is to employ the superposition of the orbits covering the large MW's space, where each of the orbit-weighting factors is assigned following the probability that the star is located at its currently observed position. The selected local, metal-poor sample with [Fe/H] <–1, using Gaia Early Data Release 3 and Sloan Digital Sky Survey Data Release 16, shows that the global shape of the halo is systematically rounder at all radii in more metal-poor ranges, such that an axial ratio, q, is nearly 1 for [Fe/H] <–2.2 and ~0.7 for –1.4 < [Fe/H] < –1.0. It is also found that a halo in the relatively metal-rich range of [Fe/H] >–1.8 actually shows a boxy/peanut-like shape, suggesting a major merger event. The distribution of azimuthal velocities shows a disk-like flattened structure at –1.4 < [Fe/H] < –1.0, which is thought to be the metal-weak thick disk. For the subsample of stars showing GSE-like kinematics, at [Fe/H] >–1.8, its global density distribution has an axis ratio of 0.9, which is more spherical than the general halo sample, and an outer ridge at r ~ 20 kpc. This spherical shape is consistent with the features of accreted halo components, and the ridge suggests that the orbit of GSE's progenitor had an apocenter of ~20 kpc. Implications for the formation of the stellar halo are also presented.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Variation of Ionizing Continuum: The Main Driver of Broad Absorption Line Variability

We present a statistical analysis of the variability of broad absorption lines (BALs) in quasars using the large multi-epoch spectroscopic data set of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We divide the sample into two groups according to the pattern of the variation of C iv BAL with respect to that of a continuum: the equivalent widths (EW) of the BAL decreases (increases) when the continuum brightens (dims) as group T1; and the variation of the EW and the continuum in the opposite relation of group T2. We find that T2 has significantly (P{sub T}<10{sup −6}, Students T Test) higher EW ratios (R) of Si iv to C iv BAL than T1. Our result agrees with the prediction of photoionization models that C{sup +3} column density increases (decreases) if there is a (or no) C{sup +3} ionization front, while R decreases with the incident continuum. We show that BAL variabilities in at least 80% of quasars are driven by the variation of an ionizing continuum, while other models that predict uncorrelated BAL and continuum variability contribute less than 20%. Considering large uncertainty in the continuum flux calibration, the latter fraction may be much smaller. When the sample is binned into different time intervals between the two observations, we find significant difference in the distribution of R between T1 and T2 in all time-bins down to ΔT<6 days, suggesting that the BAL outflow in a fraction of quasars has a recombination timescale of only a few days.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Parameter Estimation for Decoding Sensor Signals

This paper introduces a parameter estimation approach for decoding digital sensor signals in a cyber-physical system. For unknown or not fully characterized digital sensor data, it can be difficult to decipher a desired signal from background or noise. In a cyber-physical system with networked sensors, we can leverage knowledge of the physical system to inform the decoding of the digital signals. This work in progress is a case study on deciphering commercial vehicle on-board sensor networks that communicate through the Controller Area Network (CAN). By understanding the stock vehicle sensor network, a vehicle can be extended into a scalable research platform with minimal instrumentation. Our challenge was to localize desired sensor signals encoded in network traffic that included other sensor data, control messages, as well as encoding and security overhead. Due to the vehicle’s unknown sensor network, our approach developed methods to efficiently analyze and identify key signals despite the large state-space for potential signal embeddings.

Nice, Matthew↗

DOE OSTI2014

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Cyber security using multi-threaded architecture data diode at the NBSR - 114

Ongoing upgrades for the control room of the National Bureau of Standards Reactor (NBSR) located at the National Institute of Standards and Technology (NIST) require the ability to collect sensor data from the secured reactor network and transfer to the outside network for trend analysis, while maintaining the security of the reactor network. To accomplish this, a data diode was created to provide a physical barrier that prevents any outside data from reaching the secured reactor network. The hardware of the data diode consists of two embedded computers, running Unix based operating systems, connected by three DB-9 serial cables. The serial cables have been modified so that only two wires, a ground and a transmission line, provide connection between the computers. The data diode relies on a multi-threaded architecture to collect and simultaneously transfer data over three serial connections. A software program, implemented in Java, has been written to request data from the digital recorders installed to monitor the NBSR process instrumentation and transfer that data across the serial connections. The data diode can collect over 1200 data points from multiple digital recorders each second. The collected data can then be stored in a relational database for later analysis. The source codes for the software are available open source on GitHub. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

TH-A-207B-00: Shear-Wave Imaging and a QIBA US Biomarker Update

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: Introduction: Importance of tissue elasticity measurement Strain vs. shear wave elastography (SWE), beneficial features of SWE The link between shear wave speed and material properties, influence of viscosity Generation of shear waves External vibration (Fibroscan) ultrasound radiation force Point push Supersonic push (Aixplorer) Comb push (GE Logiq E9) Detection of shear waves Motion detection from pulse-echo ultrasound Importance of frame rate for shear wave imaging Plane wave imaging detection How to achieve high effective frame rate using line-by-line scanners Shear wave speed calculation Time to peak Random sample consensus (RANSAC) Cross correlation Sources of bias and variation in SWE Tissue viscosity Transducer compression or internal pressure of organ Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: Goals Review the need for quantitative medical imaging Provide examples of quantitative imaging biomarkers Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization Review the QIBA process for creating a quantitative biomarker Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile Underlying Premise and Assumptions Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice Reasons for quantification Evidence based medicine requires objective, not subjective observer data Computerized decision support tools (eg CAD) generally require quantitative input. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems What is quantitative imaging? Definition from Imaging Metrology Workshop The Quantitative Imaging Biomarker Alliance Formation 2008 Mission Structure Example Imaging Biomarkers Being Explored Biomarker Selection Groundwork Draft Protocol for imaging and data evaluation QIBA Profile Drafting Equipment and Site Validation Technical Clinical Site and Equipment QA and Compliance Checking Ultrasound Elasticity Estimation Biomarker US Elasticity Estimation Background Current Status and Problems Biomarker Selection-process and outcome US SWS for Liver Fibrosis Biomarker Work Groundwork Literature search and analysis results Phase I phantom testing-Elastic phantoms Phase II phantom testing-Viscoelastic phantoms Digital Simulated Data Protocol and Profile Drafting Protocol: based on UPICT and existing literature and standards bodies protocols Profile-Current claims, Manufacturer specific appendices What comes after the profile Profile Validation Technical validation Clinical validation QA and Compliance Possible approaches Site Operator testing Site protocol re-evaluation Imaging system Manufacturer testing and attestation User acceptance testing and periodic QA Phantom Tests Digital Phantom Based Testing Standard QA Testing Remediation Schemes Profile Evolution Towards additional applications Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-A-207B-01: Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: Introduction: Importance of tissue elasticity measurement Strain vs. shear wave elastography (SWE), beneficial features of SWE The link between shear wave speed and material properties, influence of viscosity Generation of shear waves External vibration (Fibroscan) ultrasound radiation force Point push Supersonic push (Aixplorer) Comb push (GE Logiq E9) Detection of shear waves Motion detection from pulse-echo ultrasound Importance of frame rate for shear wave imaging Plane wave imaging detection How to achieve high effective frame rate using line-by-line scanners Shear wave speed calculation Time to peak Random sample consensus (RANSAC) Cross correlation Sources of bias and variation in SWE Tissue viscosity Transducer compression or internal pressure of organ Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: Goals Review the need for quantitative medical imaging Provide examples of quantitative imaging biomarkers Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization Review the QIBA process for creating a quantitative biomarker Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile Underlying Premise and Assumptions Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice Reasons for quantification Evidence based medicine requires objective, not subjective observer data Computerized decision support tools (eg CAD) generally require quantitative input. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems What is quantitative imaging? Definition from Imaging Metrology Workshop The Quantitative Imaging Biomarker Alliance Formation 2008 Mission Structure Example Imaging Biomarkers Being Explored Biomarker Selection Groundwork Draft Protocol for imaging and data evaluation QIBA Profile Drafting Equipment and Site Validation Technical Clinical Site and Equipment QA and Compliance Checking Ultrasound Elasticity Estimation Biomarker US Elasticity Estimation Background Current Status and Problems Biomarker Selection-process and outcome US SWS for Liver Fibrosis Biomarker Work Groundwork Literature search and analysis results Phase I phantom testing-Elastic phantoms Phase II phantom testing-Viscoelastic phantoms Digital Simulated Data Protocol and Profile Drafting Protocol: based on UPICT and existing literature and standards bodies protocols Profile-Current claims, Manufacturer specific appendices What comes after the profile Profile Validation Technical validation Clinical validation QA and Compliance Possible approaches Site Operator testing Site protocol re-evaluation Imaging system Manufacturer testing and attestation User acceptance testing and periodic QA Phantom Tests Digital Phantom Based Testing Standard QA Testing Remediation Schemes Profile Evolution Towards additional applications Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-A-207B-02: QIBA Ultrasound Elasticity Imaging System Biomarker Qualification and User Testing of Systems

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: Introduction: Importance of tissue elasticity measurement Strain vs. shear wave elastography (SWE), beneficial features of SWE The link between shear wave speed and material properties, influence of viscosity Generation of shear waves External vibration (Fibroscan) ultrasound radiation force Point push Supersonic push (Aixplorer) Comb push (GE Logiq E9) Detection of shear waves Motion detection from pulse-echo ultrasound Importance of frame rate for shear wave imaging Plane wave imaging detection How to achieve high effective frame rate using line-by-line scanners Shear wave speed calculation Time to peak Random sample consensus (RANSAC) Cross correlation Sources of bias and variation in SWE Tissue viscosity Transducer compression or internal pressure of organ Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: Goals Review the need for quantitative medical imaging Provide examples of quantitative imaging biomarkers Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization Review the QIBA process for creating a quantitative biomarker Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile Underlying Premise and Assumptions Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice Reasons for quantification Evidence based medicine requires objective, not subjective observer data Computerized decision support tools (eg CAD) generally require quantitative input. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems What is quantitative imaging? Definition from Imaging Metrology Workshop The Quantitative Imaging Biomarker Alliance Formation 2008 Mission Structure Example Imaging Biomarkers Being Explored Biomarker Selection Groundwork Draft Protocol for imaging and data evaluation QIBA Profile Drafting Equipment and Site Validation Technical Clinical Site and Equipment QA and Compliance Checking Ultrasound Elasticity Estimation Biomarker US Elasticity Estimation Background Current Status and Problems Biomarker Selection-process and outcome US SWS for Liver Fibrosis Biomarker Work Groundwork Literature search and analysis results Phase I phantom testing-Elastic phantoms Phase II phantom testing-Viscoelastic phantoms Digital Simulated Data Protocol and Profile Drafting Protocol: based on UPICT and existing literature and standards bodies protocols Profile-Current claims, Manufacturer specific appendices What comes after the profile Profile Validation Technical validation Clinical validation QA and Compliance Possible approaches Site Operator testing Site protocol re-evaluation Imaging system Manufacturer testing and attestation User acceptance testing and periodic QA Phantom Tests Digital Phantom Based Testing Standard QA Testing Remediation Schemes Profile Evolution Towards additional applications Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

PlotData v. 4.47

Sandia National Laboratories has developed a computer program to review, reduce and manipulate waveform data. PlotData is designed for post-acquisition waveform data analysis. PlotData is both a post-acquisition and an advanced interactive data analysis environment. PlotData requires unidirectional waveform data with both uniform and discrete time-series measurements. PlotData operates on a National Instruments' LabVIEW™ software platform. Using PlotData, the user can capture waveform data from digitizing oscilloscopes over a GPIB, USB and Ethernet interface from Tektronix, Lecroy or Agilent scopes. PlotData can both import and export several types of binary waveform files including, but not limited to, Tektronix .wmf files, Lecroy.trc files and xy pair ASCIIfiles. Waveform manipulation includes numerous math functions, integration, differentiation, smoothing, truncation, and other specialized data reduction routines such as VISAR, POV, PVDF (Bauer) piezoelectric gauges, and piezoresistive gauges such as carbon manganin pressure gauges.

Wackerbarth, David↗