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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

WE-DE-207A-04: Advances in Radiological Neuro-Endovascular Interventional Imaging

1. Parallels in the evolution of x-ray angiographic systems and devices used for minimally invasive endovascular therapy Charles Strother - DSA, invented by Dr. Charles Mistretta at UW-Madison, was the technology which enabled the development of minimally invasive endovascular procedures. As DSA became widely available and the potential benefits for accessing the cerebral vasculature from an endovascular approach began to be apparent, industry began efforts to develop tools for use in these procedures. Along with development of catheters, embolic materials, pushable coils and the GDC coils there was simultaneous development and improvement of 2D DSA image quality and the introduction of 3D DSA. Together, these advances resulted in an enormous expansion in the scope and numbers of minimally invasive endovascular procedures. The introduction of flat detectors for c-arm angiographic systems in 2002 provided the possibility of the angiographic suite becoming not just a location for vascular imaging where physiological assessments might also be performed. Over the last decade algorithmic and hardware advances have been sufficient to now realize this potential in clinical practice. The selection of patients for endovascular treatments is enhanced by this dual capability. Along with these advances has been a steady reduction in the radiation exposure required so that today, vascular and soft tissue images may be obtained with equal or in many cases less radiation exposure than is the case for comparable images obtained with multi-detector CT. Learning Objectives: To understand the full capabilities of today’s angiographic suite To understand how c-arm cone beam CT soft tissue imaging can be used for assessments of devices, blood flow and perfusion. Advances in real-time x-ray neuro-endovascular image guidance Stephen Rudin - Reacting to the demands on real-time image guidance for ever finer neurovascular interventions, great improvements in imaging chains are being pursued. For the highest spatial and temporal resolution, x-ray guidance with fluoroscopy and angiography although dominant are still being vastly improved. New detectors such as the Micro-Angiographic Fluoroscope (MAF) and x-ray source designs that enable higher outputs while maintaining small focal spots will be highlighted along with new methods for minimizing the radiation dose to patients. Additionally, new platforms for training and device testing that include patient-specific 3D printed vascular phantoms and new metrics such as generalized relative object detectability for objectively inter-comparing systems will be discussed. This will improve the opportunity for better evaluation of these technological advances which should contribute to the safety and efficacy of image guided minimally invasive neuro-endovascular procedures. Learning Objectives: To understand the operation of new x-ray imaging chain components such as detectors and sources To be informed about the latest testing methods, with 3D printed vascular phantoms, and new evaluation metrics for advanced imaging in x-ray image guided neurovascular interventions Advances in cone beam CT anatomical and functional imaging in angio-suite to enable one-stop-shop stroke imaging workflow Guang-Hong Chen - The introduction of flat-panel detector based cone-beam CT in clinical angiographic imaging systems enabled treating physicians to obtain three-dimensional anatomic roadmaps for bony structure, soft brain tissue, and vasculatures for treatment planning and efficacy checking after the procedures. However, much improvement is needed to reduce image artifacts, reduce radiation dose, and add potential functional imaging capability to provide four-dimensional dynamic information of vasculature and brain perfusion. In this presentation, some of the new techniques developed to address radiation dose issues, image artifact reduction and brain perfusion using C-arm cone-beam CT imaging system will be introduced for the audience. Learning Objectives: To understand the clinical need of one-stop-shop stroke imaging workflow To understand to technical challenges in cone beam CT perfusion To understand the potential technical solutions to enable one-stop-shop imaging workflow Recent advances in devices used in neuro--interventions Mattew Gounis - Over the past two decades, there has been explosive development of medical devices that have revolutionized the endovascular treatment of cerebrovascular diseases. There is now Level 1, Class A evidence that intra-arterial, mechanical thrombectomy in acute ischemic stroke is superior to medical management; and similarly that minimally invasive, endovascular repair of ruptured brain aneurysms is superior to surgical treatment. Stent-retrievers are now standard of care for emergent large vessel occlusions causing a stroke, with a number of patients need to treat for good clinical outcomes as low as 4. Recent technologies such as flow diverters and disrupters, intracranial self-expanding stents, flexible large bore catheters that can reach vessels beyond the circle of Willis, stent-retrievers, and super-compliant balloons are the result of successful miniaturization of design features and novel manufacturing technologies capable of building these devices. This is a rapidly evolving field, and the device technology enabling such advancements will be reviewed. Importantly, image-guidance technology has not kept pace in neurointervention and the ability to adequately characterize these devices in vivo remains a significant opportunity. Learning Objectives: A survey of devices used in neurointerventions, their materials and essential design characteristics Funding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; G. Chen, Funding received from NIH; funding received from DOD; funding received from GE Healthcare; funding received from Siemens AX.; M. Gounis, consultant for Codman Neurovascular and Stryker Neurovascular; Holds stock in InNeuroCo Inc, research grants: NIH, Medtronic Neurovascular, Microvention/Terumo, Cerevasc LLC, Gentuity, Codman Neurovascular, Philips Healthcare, Stryker Neurovascular, Tay Sachs Foundation, and InNeuroCo Inc.; S. Rudin, Supported in part by NIH Grant R01EB002873 and the Toshiba Medical System Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Theoretical investigation of the design and performance of a dual energy (kV and MV) radiotherapy imager

Purpose: In modern radiotherapy treatment rooms, megavoltage (MV) portal imaging and kilovoltage (kV) cone-beam CT (CBCT) imaging are performed using various active matrix flat-panel imager (AMFPI) designs. To expand the clinical utility of MV and kV imaging, MV AMFPIs incorporating thick, segmented scintillators and, separately, kV imaging using a beam’s eye view geometry have been investigated by a number of groups. Motivated by these previous studies, it is of interest to explore to what extent it is possible to preserve the benefits of kV and MV imaging using a single AMFPI design, given the considerably different x ray energy spectra used for kV and MV imaging. In this paper, considerations for the design of such a dual energy imager are explored through examination of the performance of a variety of hypothetical AMFPIs based on x ray converters employing segmented scintillators. Methods: Contrast, noise, and contrast-to-noise ratio performances were characterized through simulation modeling of CBCT imaging, while modulation transfer function, Swank factor, and signal performance were characterized through simulation modeling of planar imaging. The simulations were based on a previously reported hybrid modeling technique (accounting for both radiation and optical effects), augmented through modeling of electronic additive noise. All designs employed BGO scintillator material with thicknesses ranging from 0.25 to 4 cm and element-to-element pitches ranging from 0.508 to 1.016 mm. A series of studies were performed under both kV and MV imaging conditions to determine the most advantageous imager configuration (involving front or rear x ray illumination and use of a mirror or black reflector), converter design (pitch and thickness), and operating mode (pitch-binning combination). Results: Under the assumptions of the present study, the most advantageous imager design was found to employ rear illumination of the converter in combination with a black reflector, incorporate a BGO converter with a 0.508 mm pitch and a 2 cm thickness, and operate at full resolution for kV imaging and 2 × 2 binning mode for MV imaging. Such a dual energy imager design should provide soft tissue visualization at low, clinically practical doses under MV conditions, while helping to preserve the high spatial resolution and high contrast offered by kV imaging. Conclusions: The authors’ theoretical investigation suggests that a dual energy imager capable of largely preserving the desirable characteristics of both kV and MV imaging is feasible. Such an imager, when coupled to a dual energy radiation source, could facilitate simplification of current treatment room imaging systems (as well as their associated quality assurance), and facilitate more precise integration of kV and MV imaging information by virtue of reduced geometric uncertainties.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

MO-PIS-Exhibit Hall-01: Imaging: CT Dose Optimization Technologies I

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The imaging topic this year is CT scanner dose optimization capabilities. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Dose Optimization Capabilities of GE Computed Tomography Scanners Presentation Time: 11:15 – 11:45 AM GE Healthcare is dedicated to the delivery of high quality clinical images through the development of technologies, which optimize the application of ionizing radiation. In computed tomography, dose management solutions fall into four categories: employs projection data and statistical modeling to decrease noise in the reconstructed image - creating an opportunity for mA reduction in the acquisition of diagnostic images. Veo represents true Model Based Iterative Reconstruction (MBiR). Using high-level algorithms in tandem with advanced computing power, Veo enables lower pixel noise standard deviation and improved spatial resolution within a single image. Advanced Adaptive Image Filters allow for maintenance of spatial resolution while reducing image noise. Examples of adaptive image space filters include Neuro 3-D filters and Cardiac Noise Reduction Filters. AutomA adjusts mA along the z-axis and is the CT equivalent of auto exposure control in conventional x-ray systems. Dynamic Z-axis Tracking offers an additional opportunity for dose reduction in helical acquisitions while SmartTrack Z-axis Tracking serves to ensure beam, collimator and detector alignment during tube rotation. SmartmA provides angular mA modulation. ECG Helical Modulation reduces mA during the systolic phase of the heart cycle. SmartBeam optimization uses bowtie beam-shaping hardware and software to filter off-axis x-rays - minimizing dose and reducing x-ray scatter. The DICOM Radiation Dose Structured Report (RDSR) generates a dose report at the conclusion of every examination. Dose Check preemptively notifies CT operators when scan parameters exceed user-defined dose thresholds. DoseWatch is an information technology application providing vendor-agnostic dose tracking and analysis for CT (and all other diagnostic x-ray modalities) SnapShot Pulse improves coronary CTA dose management. VolumeShuttle uses two acquisitions to increase coverage, decrease dose, and conserve on contrast administration. Color-Coding for Kids applies the Broselow-Luten Pediatric System to facilitate pediatric emergency care and reduce medical errors. FeatherLight achieves dose optimization through pediatric procedure-based protocols. Adventure Series scanners provide a child-friendly imaging environment promoting patient cooperation with resultant reduction in retakes and patient motion. Philips CT Dose Optimization Tools and Advanced Reconstruction Presentation Time: 11:45 ‘ 12:15 PM The first part of the talk will cover “Dose Reduction and Dose Optimization Technologies” present in Philips CT Scanners. The main Technologies to be presented include: DoseRight and tube current modulation (DoseRight, Z-DOM, 3D-DOM, DoseRight Cardiac) Special acquisition modes Beam filtration and beam shapers Eclipse collimator and ClearRay collimator NanoPanel detector DoseRight will cover automatic tube current selection that automatically adjusts the dose for the individual patient. The presentation will explore the modulation techniques currently employed in Philips CT scanners and will include the algorithmic concepts as well as illustrative examples. Modulation and current selection technologies to be covered include the Automatic Current Selection component of DoseRight, ZDOM longitudinal dose modulation, 3D-DOM (combination of longitudinal and rotational dose modulation), Cardiac Dose right (an ECG based dose modulation scheme), and the DoseRight Index (DRI) IQ index. The special acquisition modes covers acquisition techniques such as prospective gating that is designed to reduce exposure to the patient through the Cardiac Step and Shoot scan mode. This mode can substitute the much higher dose retrospective scan modes for certain types of cardiac imaging. The beam filtration and beam shaper portion will discuss the variety of filtration and beam shaping configurations available on Philips scanners. This topic includes the x-ray beam characteristics, tube filtration as well as dose compensator characteristics. The Eclipse collimator, ClearRay collimator and the NanoPanel detector portion will discuss additional technologies specific to wide coverage CT that address some of the unique challenges encountered and techniques employed to optimize image quality and optimize dose utilization. The Eclipse collimator reduces extraneous exposure by actively blocking the radiation tails at either end of helical scans that do not contribute to the image generation. The ClearRay collimator and the NanoPanel detector optimize the quality of the signal that reaches the detectors by addressing the increased scattered radiation present in wide coverage and the NanoPanel detector adds superior electronic noise characteristics valuable when imaging at a low dose level. The second part of the talk will present “Advanced Reconstruction Technologies” currently available on Philips CT Scanners. The talk will cover filtered back projection (FBP), iDose4 and Iterative Model Reconstruction (IMR). Each reconstruction method will include a discussion of the algorithm as well as similarities and differences between the algorithms. Examples illustrating the merits of each algorithm will be presented, and techniques and metrics to characterize the performance of each type of algorithm will be presented. The Filtered Back projection portion will discuss and provide a brief summary of relevant standard image reconstruction techniques in common use, and discuss the common tradeoffs when using the FBP algorithm. The iDose4 portion will present the algorithms used for iDose4 as well the different levels. The meaning of different levels of iDose4 available will be presented and quantified. Guidelines for selection iDose4 parameters based on the imaging need will be explained. The different image quality goals available with iDose4 and specifically how iDose4 enables noise reduction, spatial resolution improvement or both will be explained. The approaches to leveraging the benefits of iDose4 such as improved spatial resolution, decreased noise, and artifact prevention will be described and quantified; and measurements and metrics behind the improvements will be presented. The image quality benefits in specific imaging situations as well as how to best combine the technology with other dose reduction strategies to ensure the best image quality at a given dose level will be presented. Insight into the IMR algorithm as well as contrast to the iDose4 techniques and performance characteristics will be discussed. Metrics and techniques for characterizing this class of algorithm and IQ performance will be presented. The image quality benefits and the dose reduction capabilities of IMR will be explored. Illustrative examples of the noise reduction, spatial resolution improvement, and low contrast detectability improvements of the reconstruction method will be presented: clinical cases and phantom measurements demonstrating the benefits of IMR in the areas of low dose imaging, spatial resolution and low contrast resolution are discussed and the technical details behind the measurements will be presented compared to both iDose4 and traditional filtered back projection (FBP)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images

Purpose: Registration between 2D ultrasound (US) and 3D preoperative magnetic resonance (MR) (or computed tomography, CT) images has been studied recently for US-guided intervention. However, the existing techniques have some limits, either in the registration speed or the performance. The purpose of this work is to develop a real-time and fully automatic registration system between two intermodal images of the liver, and subsequently an indirect lesion positioning/tracking algorithm based on the registration result, for image-guided interventions. Methods: The proposed position tracking system consists of three stages. In the preoperative stage, the authors acquire several 3D preoperative MR (or CT) images at different respiratory phases. Based on the transformations obtained from nonrigid registration of the acquired 3D images, they then generate a 4D preoperative image along the respiratory phase. In the intraoperative preparatory stage, they properly attach a 3D US transducer to the patient’s body and fix its pose using a holding mechanism. They then acquire a couple of respiratory-controlled 3D US images. Via the rigid registration of these US images to the 3D preoperative images in the 4D image, the pose information of the fixed-pose 3D US transducer is determined with respect to the preoperative image coordinates. As feature(s) to use for the rigid registration, they may choose either internal liver vessels or the inferior vena cava. Since the latter is especially useful in patients with a diffuse liver disease, the authors newly propose using it. In the intraoperative real-time stage, they acquire 2D US images in real-time from the fixed-pose transducer. For each US image, they select candidates for its corresponding 2D preoperative slice from the 4D preoperative MR (or CT) image, based on the predetermined pose information of the transducer. The correct corresponding image is then found among those candidates via real-time 2D registration based on a gradient-based similarity measure. Finally, if needed, they obtain the position information of the liver lesion using the 3D preoperative image to which the registered 2D preoperative slice belongs. Results: The proposed method was applied to 23 clinical datasets and quantitative evaluations were conducted. With the exception of one clinical dataset that included US images of extremely low quality, 22 datasets of various liver status were successfully applied in the evaluation. Experimental results showed that the registration error between the anatomical features of US and preoperative MR images is less than 3 mm on average. The lesion tracking error was also found to be less than 5 mm at maximum. Conclusions: A new system has been proposed for real-time registration between 2D US and successive multiple 3D preoperative MR/CT images of the liver and was applied for indirect lesion tracking for image-guided intervention. The system is fully automatic and robust even with images that had low quality due to patient status. Through visual examinations and quantitative evaluations, it was verified that the proposed system can provide high lesion tracking accuracy as well as high registration accuracy, at performance levels which were acceptable for various clinical applications.

60 APPLIED LIFE 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 OSTI2023

Magnification Determination for AGHCF Metallographic Images

Metallographic images generated in the Alpha Gamma Hot Cell Facility (AGHCF) have been used for quantitative and qualitative characterization of irradiated metallic fuel pins. Individual high-magnification images (micrographs) contained in AGHCF file folders and experimental notebooks are in the form of polaroid photographs labeled with AGHCF identification number, magnification, and surface condition (as-polished or etched). Low magnification composite images (photomosaics) are also available for fuel cross sections, for radial strips from the fuel center to the cladding outer diameter, and for circumferential strips of the cladding and outer fuel region. The individual images do not have scale bars. Only a few of the composite images contain scale bars. The purpose of this work is to describe and apply methods for checking the magnification (50X to 500X) of individual images and for determining the magnification (~25X to ~100X) of composite images. The metallographic images used to verify and/or determine magnification were generated during the 1989 to 1993 timeframe for cross sections of U-10Zr/HT9 fuel rods irradiated in EBR-II and for cross sections of irradiated U-10Zr/HT9 fuel-rod samples following elevated-temperature tests conducted in the out-of-pile AGHCF Fuel Behavior Test Apparatus (FBTA). Procedures are documented in the AGHCF Operations Manual for preparation of metallographic samples, for calibration verification, and for constructing composites from individual images. Calibration verification of magnification was performed at least semiannually. One standard used for calibration verification was a glass slide with vertical lines indicating distances of 1 mm, 0.1 mm and 0.01mm. Guidance is provided in the current work for determining magnification from images of the standard. In addition to the periodic calibration verification, consistency checks are recommended to verify the magnification of images taken between the routine calibration verifications. For cladding samples with or without significant fuel-cladding chemical interaction (FCCI), images taken at different magnifications (e.g., 150X and 200X) of the unaffected cladding thickness can be compared. This process is independent of changes in cladding thickness due to swelling and creep. For images taken at only one magnification of cladding regions with no FCCI, the cladding thickness determined from the images may be compared to the nominal as-built cladding thickness (15.0 ± 0.5 mils for examples used in current work)). This procedure works best for cladding that has not experienced significant swelling and/or creep (e.g., ≤2% circumferential strain at the cladding outer surface). Composite images were constructed by pasting together higher magnification images and photographing the composite to generate a negative from which the hard-copy photograph was developed. The primary purpose of the composite images was to identify interesting areas of the fuel and cladding for imaging at higher magnification. However, procedures are recommended in the current work for determining composite magnifications to allow quantitative characterization of the fuel. Composites are not recommended for determining cladding thickness or FCCI depth because magnifications are too low (generally ≤110X) and image contrast is low. Fuel cross-section composites were created by pasting together 50X or 75X images. The nominal magnification for the photographed fuel cross-section composites is 25.4X. A more precise magnification can be determined by comparing the average cladding outer diameter measured from the composite to the average cladding outer diameter measured by profilometry. Radial-strip composites were constructed by pasting together high-magnification (150X or 250X) images. The nominal magnification of radial-strip composites photographs is about 100X. A more precise method for determining the magnification of radial strips is to compare the cladding thick

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2013

Support Routines for In Situ Image Processing

This software consists of a set of application programs that support ground-based image processing for in situ missions. These programs represent a collection of utility routines that perform miscellaneous functions in the context of the ground data system. Each one fulfills some specific need as determined via operational experience. The most unique aspect to these programs is that they are integrated into the large, in situ image processing system via the PIG (Planetary Image Geometry) library. They work directly with space in situ data, understanding the appropriate image meta-data fields and updating them properly. The programs themselves are completely multimission; all mission dependencies are handled by PIG. This suite of programs consists of: (1)marscahv: Generates a linearized, epi-polar aligned image given a stereo pair of images. These images are optimized for 1-D stereo correlations, (2) marscheckcm: Compares the camera model in an image label with one derived via kinematics modeling on the ground, (3) marschkovl: Checks the overlaps between a list of images in order to determine which might be stereo pairs. This is useful for non-traditional stereo images like long-baseline or those from an articulating arm camera, (4) marscoordtrans: Translates mosaic coordinates from one form into another, (5) marsdispcompare: Checks a Left Right stereo disparity image against a Right Left disparity image to ensure they are consistent with each other, (6) marsdispwarp: Takes one image of a stereo pair and warps it through a disparity map to create a synthetic opposite- eye image. For example, a right eye image could be transformed to look like it was taken from the left eye via this program, (7) marsfidfinder: Finds fiducial markers in an image by projecting their approximate location and then using correlation to locate the markers to subpixel accuracy. These fiducial markets are small targets attached to the spacecraft surface. This helps verify, or improve, the pointing of in situ cameras, (8) marsinvrange: Inverse of marsrange . given a range file, re-computes an XYZ file that closely matches the original. . marsproj: Projects an XYZ coordinate through the camera model, and reports the line/sample coordinates of the point in the image, (9) marsprojfid: Given the output of marsfidfinder, projects the XYZ locations and compares them to the found locations, creating a report showing the fiducial errors in each image. marsrad: Radiometrically corrects an image, (10) marsrelabel: Updates coordinate system or camera model labels in an image, (11) marstiexyz: Given a stereo pair, allows the user to interactively pick a point in each image and reports the XYZ value corresponding to that pair of locations. marsunmosaic: Extracts a single frame from a mosaic, which will be created such that it could have been an input to the original mosaic. Useful for creating simulated input frames using different camera models than the original mosaic used, and (12) merinverter: Uses an inverse lookup table to convert 8-bit telemetered data to its 12-bit original form. Can be used in other missions despite the name.

Deen, Robert G.↗
DOE OSTI2012

An image-based technique to assess the perceptual quality of clinical chest radiographs

Purpose: Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system modulation transfer function, noise power spectrum, detective quantum efficiency, and the exposure technique. While these elements form the basic underlying components of image quality, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the displayed patient images, further impacted by a particular image processing method applied, to see whether the image is suitable for diagnosis. In this paper, the authors developed a novel strategy to simulate radiologists' perceptual evaluation process on actual clinical chest images. Methods: Ten regional based perceptual attributes of chest radiographs were determined through an observer study. Those included lung grey level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Each attribute was characterized in terms of a physical quantity measured from the image algorithmically using an automated process. A pilot observer study was performed on 333 digital chest radiographs, which included 179 PA images with 10:1 ratio grids (set 1) and 154 AP images without grids (set 2), to ascertain the correlation between image perceptual attributes and physical quantitative measurements. To determine the acceptable range of each perceptual attribute, a preliminary quality consistency range was defined based on the preferred 80% of images in set 1. Mean value difference ({mu}{sub 1}-{mu}{sub 2}) and variance ratio ({sigma}{sub 1}{sup 2}/{sigma}{sub 2}{sup 2}) were investigated to further quantify the differences between the selected two image sets. Results: The pilot observer study demonstrated that our regional based physical quantity metrics of chest radiographs correlated very well with their corresponding perceptual attributes. The distribution comparisons, mean value difference estimations, and variance ratio estimations of each physical quantity between sets of images from two different techniques matched our expectation that the image quality of set 1 should be better than that of set 2. Conclusions: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Anatomical noise in contrast-enhanced digital mammography. Part II. Dual-energy imaging

Purpose: Dual-energy (DE) contrast-enhanced digital mammography (CEDM) uses an iodinated contrast agent in combination with digital mammography (DM) to evaluate lesions on the basis of tumor angiogenesis. In DE imaging, low-energy (LE) and high-energy (HE) images are acquired after contrast administration and their logarithms are subtracted to cancel the appearance of normal breast tissue. Often there is incomplete signal cancellation in the subtracted images, creating a background “clutter” that can impair lesion detection. This is the second component of a two-part report on anatomical noise in CEDM. In Part I the authors characterized the anatomical noise for single-energy (SE) temporal subtraction CEDM by a power law, with model parameters α and β. In this work the authors quantify the anatomical noise in DE CEDM clinical images and compare this with the noise in SE CEDM. The influence on the anatomical noise of the presence of iodine in the breast, the timing of imaging postcontrast administration, and the x-ray energy used for acquisition are each evaluated.Methods: The power law parameters, α and β, were measured from unprocessed LE and HE images and from DE subtracted images to quantify the anatomical noise. A total of 98 DE CEDM cases acquired in a previous clinical pilot study were assessed. Conventional DM images from 75 of the women were evaluated for comparison with DE CEDM. The influence of the imaging technique on anatomical noise was determined from an analysis of differences between the power law parameters as measured in DM, LE, HE, and DE subtracted images for each subject.Results: In DE CEDM, weighted image subtraction lowers β to about 1.1 from 3.2 and 3.1 in LE and HE unprocessed images, respectively. The presence of iodine has a small but significant effect in LE images, reducing β by about 0.07 compared to DM, with α unchanged. Increasing the x-ray energy, from that typical in DM to a HE beam, significantly decreases α by about 2 × 10{sup −5} mm{sup 2}, and lowers β by about 0.14 compared to LE images. A comparison of SE and DE CEDM at 4 min postcontrast shows equivalent power law parameters in unprocessed images, and lower α and β by about 3 × 10{sup −5} mm{sup 2} and 0.50, respectively, in DE versus SE subtracted images.Conclusions: Image subtraction in both SE and DE CEDM reduces β by over a factor of 2, while maintaining α below that in DM. Given the equivalent α between SE and DE unprocessed CEDM images, and the smaller anatomical noise in the DE subtracted images, the DE approach may have an advantage over SE CEDM. It will be necessary to test this potential advantage in future lesion detectability experiments, which account for realistic lesion signals. The authors' results suggest that LE images could be used in place of DM images in CEDM exam interpretation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

WE-A-BRD-01: MR Imaging for Treatment Planning: What Every Physicist Should Know

Ever since its introduction as a diagnostic imaging modality over 30 years ago, the radiation therapy community has acknowledged the utility of MR imaging as a tool for not only improved visualization of the target volume but also for demarcation of adjacent organs at risk. However, the adaptation of MR imaging in radiation oncology has, until recently been slow due in large part to the inability to image radiation therapy patients in their treatment position. With the introduction of so-called wide bore high field MR scanners, multi element flexible receive only RF coils, high performance imaging gradients and a range of volumetric imaging sequences it is now possible to obtain both high resolution and high signal-to-noise ratio images of in-treatment radiation therapy patients within clinically feasible imaging times. As a Result, there is renewed interest in the use of MR imaging for radiation oncology treatment planning that is being translated into physical siting and integration of these systems into radiation oncology departments. As MR imaging expands into the radiation oncology domain there is a significant and unmet need for radiation therapy physicists to become educated regarding the strengths, limitations and technical challenges associated with MR imaging. The purpose of this presentation is to address this need by providing an educational overview of the techniques and challenges associated with MR imaging of patients for radiation therapy treatment planning. As such this presentation will: 1) describe the fundamental differences between imaging of patients for diagnostic and therapeutic purposes (i.e. radiation therapy planning), 2) describe most commonly used imaging sequences and contrasts for identification of disease for radiation planning, 3) identify the most common sources of image distortion and techniques to reduce their effect on spatial fidelity of the MR data, 4) describe the effects of motion and methods to quantify/correct it, and 5) identify emergent techniques for performing MR only treatment simulation. Upon completion attendees will have a working understanding of the basic methodologies associated with MR imaging in radiation oncology, the unique technical challenges imposed by MR imaging in the treatment position and techniques to address these. Learning Objectives: 1. Understand the differences between MR imaging for diagnostic imaging and for radiation therapy planning. 2. Identify the most common sources of distortion and artifacts and simple methods to correct them. 3. Understand the challenges with MR imaging in the therapy treatment position and appropriate techniques to address them.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-AB-207A-03: Image Quality, Dose, and Clinical Applications

Practicing medical physicists are often time charged with the tasks of evaluating and troubleshooting complex image quality issues related to CT scanners. This course will equip them with a solid and practical understanding of common CT imaging chain and its major components with emphasis on acquisition physics and hardware, reconstruction, artifacts, image quality, dose, and advanced clinical applications. The core objective is to explain the effects of these major system components on the image quality. This course will not focus on the rapid-changing advanced technologies given the two-hour time limit, but the fundamental principles discussed in this course may facilitate better understanding of those more complicated technologies. The course will begin with an overview of CT acquisition physics and geometry. X-ray tube and CT detector are important acquisition hardware critical to the overall image quality. Each of these two subsystems consists of several major components. An in-depth description of the function and failure modes of these components will be provided. Examples of artifacts related to these failure modes will be presented: off-focal radiation, tube arcing, heel effect, oil bubble, offset drift effect, cross-talk effect, and bad pixels. The fundamentals of CT image reconstruction will first be discussed on an intuitive level. Approaches that do not require rigorous derivation of mathematical formulations will be presented. This is followed by a detailed derivation of the Fourier slice theorem: the foundation of the FBP algorithm. FBP for parallel-beam, fan-beam, and cone-beam geometries will be discussed. To address the issue of radiation dose related to x-ray CT, recent advances in iterative reconstruction, their advantages, and clinical applications will also be described. Because of the nature of fundamental physics and mathematics, limitations in data acquisition, and non-ideal conditions of major system components, image artifact often arise in the reconstructed images. Because of the limited scope of this course, only major imaging artifacts, their appearance, and possible mitigation and corrections will be discussed. Assessment of the performance of a CT scanner is a complicated subject. Procedures to measure common image quality metrics such as high contrast spatial resolution, low contrast detectability, and slice profile will be described. The reason why these metrics used for FBP may not be sufficient for statistical iterative reconstruction will be explained. Optimizing radiation dose requires comprehension of CT dose metrics. This course will briefly describe various dose metrics, and interaction with acquisition parameters and patient habitus. CT is among the most frequently used imaging tools due to its superior image quality, easy to operate, and a broad range of applications. This course will present several interesting CT applications such as a mobile CT unit on an ambulance for stroke patients, low dose lung cancer screening, and single heartbeat cardiac CT. Learning Objectives: Understand the function and impact of major components of X-ray tube on the image quality. Understand the function and impact of major components of CT detector on the image quality. Be familiar with the basic procedure of CT image reconstruction. Understand the effect of image reconstruction on CT image quality and artifacts. Understand the root causes of common CT image artifacts. Be familiar with image quality metrics especially high and low contrast resolution, noise power spectrum, slice sensitivity profile, etc. Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction. Be familiar with various CT dose metrics and their interaction with acquisition parameters. New development in advanced CT clinical applications. JH: Employee of GE Healthcare. FD: No disclosure.; J. Hsieh, Jiang Hsieh is an employee of GE Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-PIS-Exhibit Hall-01: Tools for TG-142 Linac Imaging QA II

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG-142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Using DoseLab to Perform TG-142 Imaging QA The goals of this session will be to present a clinical overview of acquiring images for TG-142 Imaging QA, as well as analyzing and evaluating results using DoseLab software. DoseLab supports planar imaging QA analysis using almost any QA phantom provided by numerous vendors. General advantages and disadvantages of selecting each of these phantoms will be briefly summarized. Best practices for selecting image acquisition parameters will be presented. A demonstration of using DoseLab software to perform a series of TG-142 tests will be performed. We will disuss why DoseLab uses its own set of imaging QA formulas, and why imaging QA measurement values of the same nominal properties will vary between TG- 142 software packages. Because TG-142 does not specify baseline and tolerance values for imaging QA, the presentation will recommend performing the manufacturer's acceptance test procedure to validate the equipment is functioning correctly. Afterwards, results can be obtained using the clinic's selected set of phantoms, image acquisition parameters, and TG-142 software to set proper baseline values. This presentation will highlight the reasons why comparing imaging QA results can be trickier than comparing linear accelerator treatment results and what physicists should keep in mind when comparing imaging QA results for different machines. Physicists are often unsure of the next step when there is an issue discovered during Imaging QA. Therefore, a few common examples of imaging issues and recommended solutions will be discussed. TG-142 Imaging QA Simplified: Lessons From Diagnostic Physics Collaboration There are many commercial options for the performance of imaging quality assurance tests for the linear accelerator imaging systems as required by the AAPM TG-142 report. Imaging quality assurance testing is largely performed by the diagnostic physicist so routine performance of these tests by the practicing oncology physicist can be complicated and confusing given all of the commercially available options. This presentation focuses on the performance of imaging quality assurance testing for linear accelerators using methods similar to that of diagnostic physicists. This presentation will address the ability to perform the required testing without complicated software and explores solutions for the performance of these tests in an efficient manner while still maintaining the ability to ascertain image quality changes that may ultimately affect clinical decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Development of a PET/Cerenkov-light hybrid imaging system

Purpose: Cerenkov-light imaging is a new molecular imaging technology that detects visible photons from high-speed electrons using a high sensitivity optical camera. However, the merit of Cerenkov-light imaging remains unclear. If a PET/Cerenkov-light hybrid imaging system were developed, the merit of Cerenkov-light imaging would be clarified by directly comparing these two imaging modalities. Methods: The authors developed and tested a PET/Cerenkov-light hybrid imaging system that consists of a dual-head PET system, a reflection mirror located above the subject, and a high sensitivity charge coupled device (CCD) camera. The authors installed these systems inside a black box for imaging the Cerenkov-light. The dual-head PET system employed a 1.2 × 1.2 × 10 mm{sup 3} GSO arranged in a 33 × 33 matrix that was optically coupled to a position sensitive photomultiplier tube to form a GSO block detector. The authors arranged two GSO block detectors 10 cm apart and positioned the subject between them. The Cerenkov-light above the subject is reflected by the mirror and changes its direction to the side of the PET system and is imaged by the high sensitivity CCD camera. Results: The dual-head PET system had a spatial resolution of ∼1.2 mm FWHM and sensitivity of ∼0.31% at the center of the FOV. The Cerenkov-light imaging system's spatial resolution was ∼275μm for a {sup 22}Na point source. Using the combined PET/Cerenkov-light hybrid imaging system, the authors successfully obtained fused images from simultaneously acquired images. The image distributions are sometimes different due to the light transmission and absorption in the body of the subject in the Cerenkov-light images. In simultaneous imaging of rat, the authors found that {sup 18}F-FDG accumulation was observed mainly in the Harderian gland on the PET image, while the distribution of Cerenkov-light was observed in the eyes. Conclusions: The authors conclude that their developed PET/Cerenkov-light hybrid imaging system is useful to evaluate the merits and the limitations of Cerenkov-light imaging in molecular imaging research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1995

World-Wide Web Tools for Locating Planetary Images

The explosive growth of the World-Wide Web (WWW) in the past year has made it feasible to provide interactive graphical tools to assist scientists in locating planetary images. The highest available resolution images of any site of interest can be quickly found on a map or plot, and, if online, displayed immediately on nearly any computer equipped with a color screen, an Internet connection, and any of the free WWW browsers. The same tools may also be of interest to educators, students, and the general public. Image finding tools have been implemented covering most of the solar system: Earth, Mars, and the moons and planets imaged by Voyager. The Mars image-finder, which plots the footprints of all the high-resolution Viking Orbiter images and can be used to display any that are available online, also contains a complete scrollable atlas and hypertext gazetteer to help locating areas. The Earth image-finder is linked to thousands of Shuttle images stored at NASA/JSC, and displays them as red dots on a globe. The Voyager image-finder plots images as dots, by longitude and apparent target size, linked to online images. The locator (URL) for the top-level page is http: //ic-www.arc.nasa.gov/ic/projects/bayes-group/Atlas/. Through the efforts of the Planetary Data System and other organizations, hundreds of thousands of planetary images are now available on CD-ROM, and many of these have been made available on the WWW. However, locating images of a desired site is still problematic, in practice. For example, many scientists studying Mars use digital image maps, which are one third the resolution of Viking Orbiter survey images. When they douse Viking Orbiter images, they often work with photographically printed hardcopies, which lack the flexibility of digital images: magnification, contrast stretching, and other basic image-processing techniques offered by off-the-shelf software. From the perspective of someone working on an experimental image processing technique for super-resolution, the discovery that potential users are often not using the highest resolution already available, nor using conventional image processing techniques, was surprising. This motivated the present work.

Kanefsky, Bob↗
DOE OSTI2016

Particle-image Velocimetry Sensitivity through Automatic Differentiation

A commonly encountered difficulty in modern experimental fluid dynamics experiments is the need for full-flow visualization and measurement; traditional techniques such as hot-wire probes and laser Doppler velocimetry cannot provide such information without difficult scanning procedures. Particle-image velocimetry (PIV) is a measurement technique which can overcome this spatial limitation to provide real-time three-dimensional fluid velocity vectors for an entire plane within the flow, as opposed to a single point that is provided by other measurement techniques. To accomplish this, a PIV system seeds the fluid flow with tracer particles; these must have nearly the same density as that of the fluid and a high drag coefficient to ensure that they follow the fluid flow without deviation. A laser is sent through optics to expand its beam into a sheet which illuminates the plane of interest in the fluid. The particles reflect more light than the fluid, and their image is recorded using a high speed camera. Due to limitations in bandwidth, video cannot be taken fast enough to be useful, and instead pairs of images are captured with a known separation in time. The illuminated particles will move between the two images taken by the camera, and their relative motion can be used to compute the fluid velocity at that point. When a single camera is used, only two-dimensional velocity vectors can be computed, but with the addition of another camera this can be improved to full three-dimensional vectors. The flow's time evolution can be measured by taking series of image pairs and processing each pair to get the velocity field at the time between each image pair. Only the two-dimensional version will be considered in this work. To compute the velocity vectors of the flow, the images are broken up into interrogation regions, usually in a uniform grid. In the most basic PIV algorithm, corresponding regions in the image pairs are cross-correlated to compute the most likely displacement from one image to the next. This is used since the cross correlation will achieve a maximum value when the two interrogation regions line up the best. The location of the peak in the correlation map and scaling information for the optical setup is used to compute the particle displacement from one image to the next. This process is similar to holding two semi-transparent images and moving one until they match as well as possible. Sub-pixel displacements between images can be obtained by fitting a Gaussian function to the correlation peak. Knowing the displacement vector for each set of interrogation regions in the image pair, it is a simple matter to divide by the time gap between the two images to compute the fluid velocity at the point in time and space. Since there are many interrogation regions in each image pair, the velocity field for a sheet is sampled according to the arrangement of the interrogation regions. The sensitivity of the final computed vector to the input images is difficult to compute, and has led most considerations of uncertainty in the PIV field to apply Monte Carlo methods and correlations to quantify the systematic and random errors which result from application of various PIV algorithms. In this work, automatic differentiation (AD) has been used to trace the dependence of velocity vectors computed using the correlation-peak PIV algorithm back to the input values given to an artificial image generator. These initial results form the foundation for continued work in the application of AD to algorithms in PIV. They demonstrate that AD methods can be successfully applied to PIV algorithms without undue complexity. The derivatives of each individual vector with regard to the coefficients of the velocity field used to create the artificial images can be computed and the statistics of them considered collectively, or correlated between each other to quantify the effectiveness of a PIV algorithm. With the initial implementation complete, adding extra algorithms is relatively simple and considerable data can be generated for analysis. It is hoped that application of these methods can lead to improved PIV algorithms which demonstrate better sensitivity to small variations in particle displacement. While these methods can be used as an analysis tool for refinement of new algorithms, computation of actual resultant vector sensitivities to input images' individual pixels is the next logical step of this work. Such information will be analyzed with experimental data to produce a better understanding of how output vectors' uncertainty relates to the actual image pairs, and lead to automatic bad vector removal. (authors)

42 ENGINEERING↗