Results for “Image” — Page 64

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 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↗

DOE OSTI2014

Iterative image-domain decomposition for dual-energy CT

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm is formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

WE-G-BRD-07: Automated MR Image Standardization and Auto-Contouring Strategy for MRI-Based Adaptive Brachytherapy for Cervix Cancer

Purpose: MRI-based adaptive brachytherapy (ABT) is an emerging treatment modality for patients with gynecological tumors. However, MR image intensity non-uniformities (IINU) can vary from fraction to fraction, complicating image interpretation and auto-contouring accuracy. We demonstrate here an automated MR image standardization and auto-contouring strategy for MRI-based ABT of cervix cancer. Methods: MR image standardization consisted of: 1) IINU correction using the MNI N3 algorithm, 2) noise filtering using anisotropic diffusion, and 3) signal intensity normalization using the volumetric median. This post-processing chain was implemented as a series of custom Matlab and Java extensions in MIM (v6.4.5, MIM Software) and was applied to 3D T2 SPACE images of six patients undergoing MRI-based ABT at 3T. Coefficients of variation (CV=σ/µ) were calculated for both original and standardized images and compared using Mann-Whitney tests. Patient-specific cumulative MR atlases of bladder, rectum, and sigmoid contours were constructed throughout ABT, using original and standardized MR images from all previous ABT fractions. Auto-contouring was performed in MIM two ways: 1) best-match of one atlas image to the daily MR image, 2) multi-match of all previous fraction atlas images to the daily MR image. Dice’s Similarity Coefficients (DSCs) were calculated for auto-generated contours relative to reference contours for both original and standardized MR images and compared using Mann-Whitney tests. Results: Significant improvements in CV were detected following MR image standardization (p=0.0043), demonstrating an improvement in MR image uniformity. DSCs consistently increased for auto-contoured bladder, rectum, and sigmoid following MR image standardization, with the highest DSCs detected when the combination of MR image standardization and multi-match cumulative atlas-based auto-contouring was utilized. Conclusion: MR image standardization significantly improves MR image uniformity. The combination of MR image standardization and multi-match cumulative atlas-based auto-contouring produced the highest DSCs and is a promising strategy for MRI-based ABT for cervix cancer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Denoising of polychromatic CT images based on their own noise properties

Purpose: Because of high diagnostic accuracy and fast scan time, computed tomography (CT) has been widely used in various clinical applications. Since the CT scan introduces radiation exposure to patients, however, dose reduction has recently been recognized as an important issue in CT imaging. However, low-dose CT causes an increase of noise in the image and thereby deteriorates the accuracy of diagnosis. In this paper, the authors develop an efficient denoising algorithm for low-dose CT images obtained using a polychromatic x-ray source. The algorithm is based on two steps: (i) estimation of space variant noise statistics, which are uniquely determined according to the system geometry and scanned object, and (ii) subsequent novel conversion of the estimated noise to Gaussian noise so that an existing high performance Gaussian noise filtering algorithm can be directly applied to CT images with non-Gaussian noise. Methods: For efficient polychromatic CT image denoising, the authors first reconstruct an image with the iterative maximum-likelihood polychromatic algorithm for CT to alleviate the beam-hardening problem. We then estimate the space-variant noise variance distribution on the image domain. Since there are many high performance denoising algorithms available for the Gaussian noise, image denoising can become much more efficient if they can be used. Hence, the authors propose a novel conversion scheme to transform the estimated space-variant noise to near Gaussian noise. In the suggested scheme, the authors first convert the image so that its mean and variance can have a linear relationship, and then produce a Gaussian image via variance stabilizing transform. The authors then apply a block matching 4D algorithm that is optimized for noise reduction of the Gaussian image, and reconvert the result to obtain a final denoised image. To examine the performance of the proposed method, an XCAT phantom simulation and a physical phantom experiment were conducted. Results: Both simulation and experimental results show that, unlike the existing denoising algorithms, the proposed algorithm can effectively reduce the noise over the whole region of CT images while preventing degradation of image resolution. Conclusions: To effectively denoise polychromatic low-dose CT images, a novel denoising algorithm is proposed. Because this algorithm is based on the noise statistics of a reconstructed polychromatic CT image, the spatially varying noise on the image is effectively reduced so that the denoised image will have homogeneous quality over the image domain. Through a simulation and a real experiment, it is verified that the proposed algorithm can deliver considerably better performance compared to the existing denoising algorithms.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-P-06: Moving From Computed Radiography to Digital Radiography: A Collaborative Approach to Improve Image Quality

Purpose: To bring together radiologists, technologists, and physicists to utilize post-processing techniques in digital radiography (DR) in order to optimize image acquisition and improve image quality. Methods: Sub-optimal images acquired on a new General Electric (GE) DR system were flagged for follow-up by radiologists and reviewed by technologists and medical physicists. Various exam types from adult musculoskeletal (n=35), adult chest (n=4), and pediatric (n=7) were chosen for review. 673 total images were reviewed. These images were processed using five customized algorithms provided by GE. An image score sheet was created allowing the radiologist to assign a numeric score to each of the processed images, this allowed for objective comparison to the original images. Each image was scored based on seven properties: 1) overall image look, 2) soft tissue contrast, 3) high contrast, 4) latitude, 5) tissue equalization, 6) edge enhancement, 7) visualization of structures. Additional space allowed for additional comments not captured in scoring categories. Radiologists scored the images from 1 – 10 with 1 being non-diagnostic quality and 10 being superior diagnostic quality. Scores for each custom algorithm for each image set were summed. The algorithm with the highest score for each image set was then set as the default processing. Results: Images placed into the PACS “QC folder” for image processing reasons decreased. Feedback from radiologists was, overall, that image quality for these studies had improved. All default processing for these image types was changed to the new algorithm. Conclusion: This work is an example of the collaboration between radiologists, technologists, and physicists at the University of New Mexico to add value to the radiology department. The significant amount of work required to prepare the processing algorithms, reprocessing and scoring of the images was eagerly taken on by all team members in order to produce better quality images and improve patient care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-J-129: A Strategy to Consolidate the Image Database of a VERO Unit Into a Radiotherapy Management System

Purpose: To present a strategy to integrate the imaging database of a VERO unit with a treatment management system (TMS) to improve clinical workflow and consolidate image data to facilitate clinical quality control and documentation. Methods: A VERO unit is equipped with both kV and MV imaging capabilities for IGRT treatments. It has its own imaging database behind a firewall. It has been a challenge to transfer images on this unit to a TMS in a radiation therapy clinic so that registered images can be reviewed remotely with an approval or rejection record. In this study, a software system, iPump-VERO, was developed to connect VERO and a TMS in our clinic. The patient database folder on the VERO unit was mapped to a read-only folder on a file server outside VERO firewall. The application runs on a regular computer with the read access to the patient database folder. It finds the latest registered images and fuses them in one of six predefined patterns before sends them via DICOM connection to the TMS. The residual image registration errors will be overlaid on the fused image to facilitate image review. Results: The fused images of either registered kV planar images or CBCT images are fully DICOM compatible. A sentinel module is built to sense new registered images with negligible computing resources from the VERO ExacTrac imaging computer. It takes a few seconds to fuse registered images and send them to the TMS. The whole process is automated without any human intervention. Conclusion: Transferring images in DICOM connection is the easiest way to consolidate images of various sources in your TMS. Technically the attending does not have to go to the VERO treatment console to review image registration prior delivery. It is a useful tool for a busy clinic with a VERO unit.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

WE-G-18A-05: Cone-Beam CT Reconstruction with Deformed Prior Image

Purpose: Prior image can be incorporated into image reconstruction process to improve the quality of on-treatment cone-beam CT (CBCT) from sparseview or low-dose projections. However, the deformation between the prior image and on-treatment CBCT are not considered in current prior image based reconstructions (e.g., prior image constrained compressed sensing (PICCS)). The purpose of this work is to develop a deformed-prior-imagebased- reconstruction strategy (DPIR) to address the mismatch problem between the prior image and target image. Methods: The deformed prior image is obtained by a projection based registration approach. Specifically, the deformation vector fields (DVF) used to deform the prior image is estimated through matching the forward projection of the prior image and the measured on-treatment projection. The deformed prior image is then used as the prior image in the standard PICCS algorithm. Simulation studies on the XCAT phantom was conducted to evaluate the performance of the projection based registration procedure and the proposed DPIR strategy. Results: The deformed prior image matches the geometry of on-treatment CBCT closer as compared to the original prior image. Using the deformed prior image, the quality of the image reconstructed by DPIR from few-view projection data is greatly improved as compared to the standard PICCS algorithm. The relative image reconstruction error is reduced to 11.13% in the proposed DPIR from 17.57% in the original PICCS. Conclusion: The proposed DPIR approach can solve the mismatch problem between the prior image and target image, which overcomes the limitation of the original PICCS algorithm for CBCT reconstruction from sparse-view or low-dose projections.

60 APPLIED LIFE SCIENCES↗