Search NASA⌕ Search

SEARCH · Search NASA

Results for “additional protocol”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Objective characterization of GE Discovery CT750 HD scanner: Gemstone spectral imaging mode

Purpose: To objectively characterize the performance of the gemstone spectral imaging (GSI) mode of GE CT750 HD scanner from a user's perspective. Methods: A regular scan protocol that approximates the adult abdomen scan protocol frequently used in the authors' institute was selected as the baseline, and a GSI protocol (preset 11) that is similar to the regular protocol and has a moderate dose level (CTDI{sub vol}=26.27 mGy) was compared to the baseline protocol. The resolving power of both protocols was characterized in terms of modulation transfer functions and high contrast resolution bar readings. Their noise characteristics were studied through noise power spectra, and their low contrast detectability was compared via contrast-to-noise ratio. Material decomposition capability of GSI was evaluated by scanning iodine solutions of 9-24 mg/ml iodine concentration in a Gammex CT phantom and by examining the estimated iodine concentration. In addition, a formula describing the dependency of HU in iodine enhanced area on GSI monochromatic energies and iodine concentrations was provided and the theoretical values were compared with the measured results. Results: The resolutions levels of 50%, 10%, and 5% MTF of GSI monochromatic images at 65 keV agree with those of the regular protocol within 0.1 lp/cm. GSI monochromatic images at 65 keV demonstrated the lowest noise level among GSI images of different monochromatic energies and showed very similar noise magnitude and noise power distribution as compared to the regular protocol images. The CNR of 60 and 65 keV GSI monoimages are approximately 100% of those of the regular protocol images. Estimated iodine concentration levels agreed with the actual values within 2% when the iodine solutions were placed at 3, 9, 12 o'clock positions of the phantom; when iodine solutions were placed at the phantom center and at 6 o'clock position, higher discrepancies of 2%-10% were observed. The observed dependency of HU on keV and iodine concentration levels agreed with the expectation from x-ray attenuations. Conclusions: Equivalent performances were observed in the comparison between GSI 65 keV monochromatic images and images from a regular abdomen scan protocol. This suggests the possibility of GSI to be employed in routine abdominal scans, which would potentially offer more information through its capabilities of material decomposition.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Graphene oxide supported tin dioxide: synthetic approaches and electrochemical characterization as anodes for lithium- and sodium-ion batteries

The review addresses synthetic approaches to composite materials based on graphene oxide and nano tin dioxide and their electrochemical properties as anodes for lithium- and sodiumion batteries. The introduction of a carbon matrix into the composite material improves the electrochemical characteristics of the anodes. In most methods, the synthesis of graphene oxide–tin dioxide composites is based on the use of tin(II,IV) chlorides as the starting compounds, and the most efficient electrode materials were obtained by the hydrothermal or solvothermal routes. Thermal processing is much more economic than the gas phase deposition protocols but requires heating of a large volume of dilute tin oxide dispersions in an autoclave. Mechanochemistry (ball milling) is also economically unfavorable for the synthesis of composite materials. In addition, large volumes of acidic wastes that should be neutralized and safely discarded are formed when tin chlorides are used. An alternative environmentally friendly technique based on the use of aqueous peroxide solutions can be applied for the production of efficient anode materials based on graphene oxide and tin dioxide. This process does not involve acidic wastes, uses hydrogen peroxide and ethanol as reagents, and accomplishes film deposition (coating) at room temperature. Final thermal treatment is required only for the active material, which minimizes energy expenses and equipment costs.

25 ENERGY STORAGE↗
DOE OSTI2011

Extended Survival of Several Microorganisms and Relevant Amino Acid Biomarkers under Simulated Martian Surface Conditions as a Function of Burial Depth

Recent orbital and landed missions have provided substantial evidence for ancient liquid water on the martian surface as well as evidence of more recent sedimentary deposits formed by water and/or ice. These observations raise serious questions regarding an independent origin and evolution of life on Mars. Future missions seek to identify signs of extinct martian biota in the form of biomarkers or morphological characteristics, but the inherent danger of spacecraft-borne terrestrial life makes the possibility of forward contamination a serious threat not only to the life detection experiments, but also to any extant martian ecosystem. A variety of cold and desiccation-tolerant organisms were exposed to 40 days of simulated martian surface conditions while embedded within several centimeters of regolith simulant in order to ascertain the plausibility of such organisms survival as a function of environmental parameters and burial depth. Relevant amino acid biomarkers associated with terrestrial life were also analyzed in order to understand the feasibility of detecting chemical evidence for previous biological activity. Results indicate that stresses due to desiccation and oxidation were the primary deterrent to organism survival, and that the effects of UV-associated damage, diurnal temperature variations, and reactive atmospheric species were minimal. Organisms with resistance to desiccation and radiation environments showed increased levels of survival after the experiment compared to organisms characterized as psychrotolerant. Amino acid analysis indicated the presence of an oxidation mechanism that migrated downward through the samples during the course of the experiment and likely represents the formation of various oxidizing species at mineral surfaces as water vapor diffused through the regolith. Current sterilization protocols may specifically select for organisms best adapted to survival at the martian surface, namely species that show tolerance to radical-induced oxidative damage and low water activity environments. Additionally, any hypothetical martian ecosystems may have evolved similar physiological traits that allow sporadic metabolism during periods of increased water activity.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2013

Summary Statistics for LC_TA03_130719: Micro-CT Data Acquired at LLNL, Specimen 3 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, lithium carbonate (LC). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, LC_TA03_130719, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images 2 . The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MP03_130620: Micro-CT Data Acquired at LLNL, Specimen 3 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MP03_130620, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images 2 . The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MH03_130828: Micro-CT Data Acquired at LLNL, Specimen 3 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MH03_130828, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MH01_130826: Micro-CT Data Acquired at LLNL, Specimen 1 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MH01_130826, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MH02_130827: Micro-CT Data Acquired at LLNL, Specimen 2 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MH03_130827, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MC03_130806: Micro-CT Data Acquired at LLNL, Specimen 3 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MC03_130806, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MC02_130701: Micro-CT Data Acquired at LLNL, Specimen 2 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MC02_130701, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_ST01_130731: Micro-CT Data Acquired at LLNL, Specimen 1 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_ST01_130731, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_SH02_130820: Micro-CT Data Acquired at LLNL, Specimen 2 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_SH02_130820, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MT03_130730: Micro-CT Data Acquired at LLNL, Specimen 3 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MT03_130730, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_SH01_130819: Micro-CT Data Acquired at LLNL, Specimen 1 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_SH01_130819, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗
DOE OSTI2013

Summary Statistics for TA_MT01_130725: Micro-CT Data Acquired at LLNL, Specimen 1 of 3

Using x-ray micro computed tomography (MicroCT), we have characterized the linear attenuation coefficients (LAC), μ, of a sample of a dry powder material, tartaric acid (TA). The specimen was prepared at Lawrence Livermore National Laboratory (LLNL), loaded into a 60mL low density polyethylene (LDPE) bottle. After completed packing, the specimen was scanned following the protocol for MicroCT measurements under Test Plan 79. This particular specimen, TA_MT01_130725, recorded the bulk packing density (mass of sample divided by volume of sample) shown above. Two additional preparations were made and analyzed. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the x-ray data capture and image reconstruction are given in this report. Additional experimental details may be found in the SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an ~80% region or segment of volume elements (“voxels”) lying completely within the sample, away from the walls of the container. We then calculated the mean value, standard deviation and entropy for (a) the high and low energy image segments and for (b) their digital gradient images. The statistics of the initial image of LAC values are called “first order statistics;” those of the gradient image, “second order statistics.” See Seetho for details of the analysis used to obtain the numbers reported in this document.

42 ENGINEERING↗