Performance Evaluation of CT Systems
|
|
- Randell Chandler
- 6 years ago
- Views:
Transcription
1 8/2/206 Performance Evaluation of CT Systems AAPM Task Group 233 Foundations, Methods, and Prospects Ehsan Samei Duke University Medical Center Disclosures Research grant: NIH R0 EB00838 Research grant: General Electric Research grant: Siemens Medical Advisory board: Imologix TG 233 membership Donovan Bakalyar Kirsten L Boedeker Samuel Brad Jiahua Fan Shuai Leng Michael McNitt-Gray Kyle J. Myers Lucretiu Popescu Juan Carlos Ramirez Giraldo Frank Ranallo Ehsan Samei Justin Solomon Jay Vaishnav Jia Wang
2 8/2/206 Duke Imaging Physics Carl E Ravin Advanced Imaging Laboratories Clinical Imaging Physics Group Outline Foundation What has led to TG233 Methods Select procedures Prospects Possibilities Future extensions Outline Foundation What has led to TG233 Methods Select procedures Prospects Possibilities Future extensions 2
3 8/2/206 Foundation New physics perspective New CT features New physics perspective Medical physics metrology is most relevant to the extend it relates to the clinical need. Conformance-based testing Validating physical specifications: pass/fail Relevance of non-compliance? 2. Performance-based testing Metrics relevant to clinical accuracy Application to optimization of use: precision medicine and patient-centric care Medical Physics 3.0 Medical physics extending from specifications to performance equipment to operation quality check to process consistency Presumption to actual utility compliance to excellence 3
4 8/2/206 Features of modern CT Variable kernels Iterative reconstruction Tube current modulation Special applications Spectral methods and applications Cone-beam CT Cardiac CT Perfusion imaging Iterative recons ASiR (GE) Veo (GE) IRIS (Siemens) SAFIRE (Siemens) AIDR (Toshiba) idose (Philips) Significant potential for dose reduction Potential for improved image quality Increased vendor-dependence Unconventional image appearance Limited utility of prior quality metrics Need for nuanced implementation for effective improvement in patient care FBP Reconstruction Iterative Reconstruction courtesy of University of Erlangen, Germany 4
5 MTF NPS MTF NPS 8/2/206 FBP Reconstruction Iterative Reconstruction Low Dose 4 DLP,.9 msv Courtesy of Dr de Mey and Dr Nieboer, UZ Brussel, Belgium Resolution and noise, eg Comparable resolution Lower noise but different texture -FBP -IRIS -SAFIRE x FBP -IRIS -SAFIRE Spatial frequency Spatial frequency Resolution and noise, eg 2 Higher resolution Lower noise but different texture 0.9 x MBIR -ASIR -FBP MBIR -ASIR -FBP Spatial frequency Spatial frequency 5
6 8/2/206 Non-Stationary Noise and Resolution The Local NPS and MTF NPS MTF 2.5 x Noise and resolution model for Penalized Likelihood (PL) model-based reconstruction.* Predictive framework for NPS, MTF, and detectability index (d ) enables task-based design and optimization of new systems using iterative reconstruction. *Fessler et al. IEEE-TIP (996) G. Gang et al. Med Phys 4 (204) Outline Foundation What has led to TG233 Methods Select procedures Prospects Possibilities Future extensions Methods. Visual inspection 2. Basic performance Summarizing existing methods in tabular form 3. Operational performance Tube current modulation Spatial resolution Noise Quasi-linear task-based performance Spatial domain task-based performance Appendix 6
7 8/2/206 Basic performance TCM Use: Variable-sized, continuous and step-wise phantoms imaged with TCM Measure: ma and noise per size Evaluate: ma and noise size dependencies Phase concordance Resolution Use: Phantom with 2 cm circular inserts of relevant contrast imaged using representative protocols Measure: Task transfer function (TTF) Evaluate: TTF at defined noise and contrast Frequencies at 50% and 0% TTF (f 50 and f 0 ) 7
8 8/2/206 Noise Use: Variable-sized, uniform phantom imaged using representative protocols Measure: Noise magnitude and NPS Evaluate: SD and NPS at defined noise levels Peak, average frequencies of the NPS (f P and f A ) Spatial-domain task-based detectability Use: Uniform phantoms with rod and sphere targets imaged using representative protocols Measure: Human or observer model target detection Evaluate: Localization success rate for targeted tasks Area under the LROC curve for targeted tasks Area under the EFROC curve for targeted tasks Quasi-linear task-based detectability index Use: TTF and NPS evaluation phantom imaged using representative protocols Measure: TTF and NPS Evaluate: Detectability indices for reference tasks (, 5, 0 mm, 0 and 00 HU, designer, rect, Gaussian) ' ( d NPWE ) 2 = òò [ òò MTF 2 (u,v)w 2 Task (u,v) E 2 (u,v)dudv] 2 MTF 2 2 (u,v)w Task (u,v) NPS(u,v)E 4 (u,v) + MTF 2 2 (u,v)w Task (u,v)n i dudv 8
9 37 cm 30 cm 23 cm 8.5 cm 2 cm 8/2/206 Suggested reference protocols Quasi-linear task-based measurements Mercury Phantom 3.0 Size matching population cohorts Designed for size, AEC, and d evaluations Mercury Phantom cm 6 cm 6 cm 6 cm 6 cm 3.9 cm 8.5 cm 5.63 cm 6 cm 3 cm 30 9
10 8/2/206 Design: Size Pediatric representation percentages MP 3.0 section Water size equivalent 20 mm 2 mm 85 mm 77 mm Abdomen Chest Head Age Percentile Age Percentile Age Percentile mm 220 mm mm 290 mm mm 355 mm Design: Size Adult representation percentages MP 3.0 section Water size equivalent Abdomen Chest Head M F M F M F 20 mm 2 mm mm 77 mm mm 220 mm mm 290 mm mm 355 mm Design: Resolution, HU, noise Representation of abnormality-relevant HUs Sizes large enough for resolution sampling Maximum margin for individual assessment Iso-radius resolution properties Matching uniform section for noise assessment 30 0
11 8/2/206 Image Quality Evaluation Software HU, Contrast, Noise, CNR, MTF, NPS, d per patient size, fixed ma and TCM Expected release through AAPM TG 233 d vs observer performance Christianson et al, Radiology, 205 Comparing observer models Model: CNR CNRa NPW NPWE CHO CHOi Task Properties Lesion Contrast x x x x x x Lesion Size x x x x x Image Properties Noise Magnitude x x x x x x Noise Texture x x x x Resolution x x x x Observer Properties Visual System x x x Observer Noise x Assumptions Quasi LSI System x x Noise Stationarity x x
12 Detection Accuracy Detection Accuracy Detection Accuracy Detection Accuracy Detection Accuracy Detection Accuracy Human 8/2/206 Criteria for goodness of d calculation methods Strong correlation with human results Coefficient of determination (R 2 ) used as goodness of fit metric. Correlation independent of reconstruction algorithm Error, E, of linear discriminator used (bigger = better). Confidence interval for d should be small Average CI 95% used. Normalized to unity slope FBP ADMIRE Fit Lin. Disc. Model (c) Ehasn Samei R 2 = 0.4 R 2 = 0.4 R 2 = 0.4 E = 0.3E = 0.3 E = 0.3 CI = CI 5.60e-03 = 95% CI 5.60e-03 95% = 5.60e-03 95% Simple Metrics Fourier-Based Image Based CNR CNR CNR NPW NPW NPW Humans R R Rvs. Models 2 = 26 2 = 26 2 = 26 E = 0.25E = 0.25 E = 0.25 CI = CI 3.32e-03 = 95% CI 3.32e-03 95% = 3.32e-03 95% CHO CHO CHO R 2 = 03 R 2 = 03 R 2 = 03 E = 0.4E = 0.4 E = 0.4 CI = CI 4.25e-02 = 95% CI 4.25e-02 95% = 4.25e-02 95% A A CNR CNR A CNR A A NPW NPW A NPW A A CHO CHO A CHO CNRa CNRa CNRa NPWE NPWE NPWE CHOi CHOi CHOi 0.9 R 2 = 07 R 2 = 07 R 2 = 07 E = 0.5E = 0.5 E = 0.5 CI = CI.20e-02 = 95% CI.20e-02 =.20e-02 95% 95% R 2 = 7 R 2 = 7 R 2 = 7 E = 0.3E = 0.3 E = 0.3 CI = CI 5.83e-03 = 95% CI 5.83e-03 = 5.83e-03 95% 95% R 2 = 2 R 2 = 2 R 2 = 2 E = 0.45E = 0.45 E = 0.45 CI = CI 4.59e-02 = 95% CI 4.59e-02 = 4.59e-02 95% 95% A A A CNRa CNRa CNRa A A A NPWE NPWE NPWE A A A CHOi CHOi CHOi Comparing statistics across models R CNR CNRa NPW NPWE CHO CHOi CNR CNRa NPW NPWE CHO CHOi E CI 95% CNR CNRa NPW NPWE CHO CHOi 2
13 Iodine concentration 8/2/206 No Somewhat Yes Model Comparison CNR CNRa NPW NPWE CHO CHOi Correlated with humans? Computed for generic tasks not present in phantom? Handling non-linearity or nonstationary noise? Correctly characterizing different recons? Acceptable uncertainty for a reasonable # of images? Outline Foundation What has led to TG233 Methods Select procedures Prospects Possibilities Future extensions Task-based performance Task characteristics for detection and estimation Size F C (r ) = C peak ( - r 2 æ ö ç èr ø ) n 3
14 AUC (NPWE) AUC (NPWE) 20% 29% 8/2/206 AUC vs. dose FBP IRIS SAFIRE3 SAFIRE5 Chen, SPIE, 203 Task-based dose reduction Large feature detection task 0.95 MBIR ACR Acrylic insert % FBP Eff dose (msv) 0.2 msv 3.7 msv Task-based dose reduction Small feature detection task 0.95 MBIR ACR lp/mm bar pattern % FBP Eff dose (msv) 0.2 msv 3.7 msv 4
15 Detectability 8/2/206 Dose-quality optimization Quality-dose gradient to achieve highest quality at lowest dose Iso-gradient operating points 2 Detectability index across systems 0 8 All Systems System System 2 System 3 System 4 System 5 System 6 System 7 System 8 System 9 System 0 System System 2 System Date Intra system variability: -4% Inter system variability: 6% Detectability index across protocols pilot national trial # Protocols
16 Average resolution (f 50 ) 8/2/206 Protocol optimization: Noise texture matching GE Siemens Solomon, Samei, Med Phys, 202 Texture similarity Sharpness Solomon, Samei, Med Phys, 202 Protocol optimization: Noise texture and resolution matching Average noise texture (f A ) 6
17 Average resolution (f 50 ) Log of dose needed for a target noise of 0 8/2/206 Protocol optimization: Noise texture and resolution matching Average noise texture (f A ) Outline Foundation What has led to TG233 Methods Select procedures Prospects Possibilities Future extensions CT performance in anatomicallyinformed textured phantoms Lung Texture Soft-Tissue Texture 7
18 NPS nnps (HU (mm 2 *mm 2 ) 2 ) 8/2/206 Noise in textured phantoms What about noise texture? FBP Lung Texture NPS IR FBP: Red Pixels IR: Red Pixels IR: Blue Pixels Spatial Frequency (cycles/mm) 8
19 8/2/206 Texture phantom library Cylinder phantom 30 mm thick 65 mm diameter 20 Low contrast signals size (6 mm) 5 contrast levels (~3, 5, 7, 0, 4 HU) Signal-present and signal-absent regions with identical background 4 phantoms made 3 textured + uniform 57 Conclusions New technologies and new paradigm necessitate an upgrade to performance metrology towards higher degrees of clinical relevance: Taskful surrogates of clinical performance Application for use optimization TG233 a first step towards uniformity and relevance of characterization TG 233 timeline Aug 206: v. 2 released for committee review Release anticipated in late 206 9
Disclosures 7/21/2014. AAPM 2014 Challenges and opportunities in assessment of image quality
7/2/24 AAPM 24 Challenges and opportunities in assessment of image quality Ehsan Samei Department of Radiology Duke University Medical Center Disclosures Research grant: NIH R EB838 Research grant: General
More informationLimits of Dose Reduction in CT: How low is too low? Acknowledgement. Disclosures 8/2/2012
8/2/22 Limits of Dose Reduction in CT: How low is too low? Ehsan Samei Duke University Acknowledgement Disclosures Research grant: NIH Research grant: DHS Research grant: RSNA, QIBA Research grant: General
More information8/2/2016. Acknowledgement. Common Clinical Questions. Presumption Images are Good Enough to accurately answer clinical questions
Image Quality Assessment using Model Observers: Clinical Implementation and Practical Considerations Shuai Leng, PhD Associate Professor, Department of Radiology Mayo Clinic, Rochester MN Acknowledgement
More informationAssessment of 3D performance metrics. X-ray based Volumetric imaging systems: Fourier-based imaging metrics. The MTF in CT
Assessment of 3D performance metrics D and 3D Metrics of Performance Towards Quality Index: Volumetric imaging systems X-ray based Volumetric imaging systems: CBCT/CT Tomosynthesis Samuel Richard and Ehsan
More information8/2/2016. Measures the degradation/distortion of the acquired image (relative to an ideal image) using a quantitative figure-of-merit
Ke Li Assistant Professor Department of Medical Physics and Department of Radiology School of Medicine and Public Health, University of Wisconsin-Madison This work is partially supported by an NIH Grant
More informationCT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION
CT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION Frank Dong, PhD, DABR Diagnostic Physicist, Imaging Institute Cleveland Clinic Foundation and Associate Professor of Radiology
More information1. Deployment of a framework for drawing a correspondence between simple figure of merits (FOM) and quantitative imaging performance in CT.
Progress report: Development of assessment and predictive metrics for quantitative imaging in chest CT Subaward No: HHSN6801000050C (4a) PI: Ehsan Samei Reporting Period: month 1-18 Deliverables: 1. Deployment
More informationCorrelation between Model and Human Observer Performance on a Lesion Shape Discrimination Task in CT
Correlation between Model and Human Observer Performance on a Lesion Shape Discrimination Task in CT Yi Zhang, Shuai Leng, Lifeng Yu and Cynthia McCollough Department of Radiology Mayo Clinic, Rochester
More informationSpiral ASSR Std p = 1.0. Spiral EPBP Std. 256 slices (0/300) Kachelrieß et al., Med. Phys. 31(6): , 2004
Spiral ASSR Std p = 1.0 Spiral EPBP Std p = 1.0 Kachelrieß et al., Med. Phys. 31(6): 1623-1641, 2004 256 slices (0/300) Advantages of Cone-Beam Spiral CT Image quality nearly independent of pitch Increase
More informationIterative Reconstructions: The Impact on Dose and Image Quality
Shane Foley: IPR, Chicago 2016 Iterative Reconstructions: The Impact on Dose and Image Quality Shane Foley, PhD Radiography and Diagnostic Imaging School of Medicine University College Dublin. Radagrafaíocht
More information7/13/2015 EVALUATION OF NONLINEAR RECONSTRUCTION METHODS. Outline. This is a decades-old challenge
EVALUATION OF NONLINEAR RECONSTRUCTION METHODS Kyle J. Myers, Ph.D. Director, Division of Imaging, Diagnostics, and Software Reliability Office of Science and Engineering Laboratories, CDRH, FDA 2 Outline
More informationAIDR 3D Iterative Reconstruction:
Iterative Reconstruction: Integrated, Automated and Adaptive Dose Reduction Erin Angel, PhD Manager, Clinical Sciences, CT Canon Medical Systems USA Iterative Reconstruction 1 Since the introduction of
More informationCT Iterative Reconstruction Techniques
RECENT ADVANCES IN CT RADIATION DOSE REDUCTION TECHNIQUES CT Iterative Reconstruction Techniques Kalpana Kanal, PhD, FSCBTMR, FACR, FAAPM University of Washington Seattle, WA SCBT-MR 2017 Nashville, Tennessee
More information3/27/2012 WHY SPECT / CT? SPECT / CT Basic Principles. Advantages of SPECT. Advantages of CT. Dr John C. Dickson, Principal Physicist UCLH
3/27/212 Advantages of SPECT SPECT / CT Basic Principles Dr John C. Dickson, Principal Physicist UCLH Institute of Nuclear Medicine, University College London Hospitals and University College London john.dickson@uclh.nhs.uk
More informationOptimization of CT Simulation Imaging. Ingrid Reiser Dept. of Radiology The University of Chicago
Optimization of CT Simulation Imaging Ingrid Reiser Dept. of Radiology The University of Chicago Optimization of CT imaging Goal: Achieve image quality that allows to perform the task at hand (diagnostic
More informationOptimisation of Toshiba Aquilion ONE Volume Imaging
Optimisation of Toshiba Aquilion ONE Volume Imaging Jane Edwards, RPRSG Royal Free London NHS Foundation Trust Dr Mufudzi Maviki, Plymouth Hospitals NHS Trust Background In 2011/12 Radiology at RFH was
More informationAcknowledgments and financial disclosure
AAPM 2012 Annual Meeting Digital breast tomosynthesis: basic understanding of physics principles James T. Dobbins III, Ph.D., FAAPM Director, Medical Physics Graduate Program Ravin Advanced Imaging Laboratories
More informationAgenda : Lung Density Breakout Session
Agenda : Lung Density Breakout Session 1. : Mathew Fuld and Bernice Hoppel 2. Automatic Exposure Control (AEC) Evaluation : Sean Fain Round 4 Project 3. Dose reduction effects on emphysema metrics : Philip
More informationAAPM Standard of Practice: CT Protocol Review Physicist
AAPM Standard of Practice: CT Protocol Review Physicist Dianna Cody, Ph.D., DABR, FAAPM U.T.M.D. Anderson Cancer Center September 11, 2014 2014 Texas Radiation Regulatory Conference Goals Understand purpose
More informationComputed Tomography Imaging: CT Protocol Management. Caveat 8/3/2017
Computed Tomography Imaging: CT Protocol Management Mark P. Supanich, Ph.D., DABR AAPM Annual Meeting 3 rd August, 2017 Slides at: goo.gl/k8n8jf Rush is an academic health system comprising Rush University
More informationAcknowledgments. Nesterov s Method for Accelerated Penalized-Likelihood Statistical Reconstruction for C-arm Cone-Beam CT.
June 5, Nesterov s Method for Accelerated Penalized-Likelihood Statistical Reconstruction for C-arm Cone-Beam CT Adam S. Wang, J. Webster Stayman, Yoshito Otake, Gerhard Kleinszig, Sebastian Vogt, Jeffrey
More informationBackground. Outline. Radiographic Tomosynthesis: Image Quality and Artifacts Reduction 1 / GE /
Radiographic Tomosynthesis: Image Quality and Artifacts Reduction Baojun Li, Ph.D Department of Radiology Boston University Medical Center 2012 AAPM Annual Meeting Background Linear Trajectory Tomosynthesis
More informationSpectral analysis of non-stationary CT noise
Spectral analysis of non-stationary CT noise Kenneth M. Hanson Los Alamos Scientific Laboratory Int. Symposium and Course on Computed Tomography, Las Vegas, April 7-11, 1980 This presentation available
More informationThe Near Future in Cardiac CT Image Reconstruction
SCCT 2010 The Near Future in Cardiac CT Image Reconstruction Marc Kachelrieß Institute of Medical Physics (IMP) Friedrich-Alexander Alexander-University Erlangen-Nürnberg rnberg www.imp.uni-erlangen.de
More informationMetal Artifact Reduction CT Techniques. Tobias Dietrich University Hospital Balgrist University of Zurich Switzerland
Metal Artifact Reduction CT Techniques R S S S Tobias Dietrich University Hospital Balgrist University of Zurich Switzerland N. 1 v o 4 1 0 2. Postoperative CT Metal Implants CT is accurate for assessment
More informationA noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, VOLUME 17, NUMBER 5, 2016 A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0 Guang Li, 1 Xinming Liu, 1 Cristina T.
More informationCT Protocol Review: Practical Tips for the Imaging Physicist Physicist
CT Protocol Review: Practical Tips for the Imaging Physicist Physicist Dianna Cody, Ph.D., DABR, FAAPM U.T.M.D. Anderson Cancer Center August 8, 2013 AAPM Annual Meeting Goals Understand purpose and importance
More informationConflicts of Interest Nuclear Medicine and PET physics reviewer for the ACR Accreditation program
James R Halama, PhD Loyola University Medical Center Conflicts of Interest Nuclear Medicine and PET physics reviewer for the ACR Accreditation program Learning Objectives 1. Be familiar with recommendations
More informationCardiac Dual Energy CT: Technique
RSNA 2013, VSCA51-01, Chicago, Dec. 5, 2013 Cardiac Radiology Series Cardiac Dual Energy CT: Technique Willi A. Kalender, Ph.D. Institute of Medical Physics University of Erlangen www.imp.uni-erlangen.de
More informationLow-Dose Dual-Energy CT for PET Attenuation Correction with Statistical Sinogram Restoration
Low-Dose Dual-Energy CT for PET Attenuation Correction with Statistical Sinogram Restoration Joonki Noh, Jeffrey A. Fessler EECS Department, The University of Michigan Paul E. Kinahan Radiology Department,
More informationSpiral CT. Protocol Optimization & Quality Assurance. Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA
Spiral CT Protocol Optimization & Quality Assurance Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA Spiral CT Protocol Optimization & Quality Assurance Protocol optimization
More information8/1/2017. Current Technology: Energy Integrating Detectors. Principles, Pitfalls and Progress in Photon-Counting-Detector Technology.
Photon Counting Detectors and Their Applications in Medical Imaging Principles, Pitfalls and Progress in Photon-Counting-Detector Technology Taly Gilat Schmidt, PhD Associate Professor Department of Biomedical
More informationPrecision of Iodine Quantification in Hepatic CT: Effects of Iterative Reconstruction With Various Imaging Parameters
Medical Physics and Informatics Original Research Iterative Reconstruction Algorithms in Hepatic CT Medical Physics and Informatics Original Research Baiyu Chen 1,2 Daniele Marin 3 Samuel Richard 2,3,4
More informationEffects of the difference in tube voltage of the CT scanner on. dose calculation
Effects of the difference in tube voltage of the CT scanner on dose calculation Dong Joo Rhee, Sung-woo Kim, Dong Hyeok Jeong Medical and Radiological Physics Laboratory, Dongnam Institute of Radiological
More informationSelf-Calibration of Cone-Beam CT Using 3D-2D Image Registration
Self-Calibration of Cone-Beam CT Using 3D-2D Image Registration Sarah Ouadah 1 J. Webster Stayman 1 Grace Jianan Gang 1 Ali Uneri 2 Tina Ehtiati 3 Jeffrey H. Siewerdsen 1,2 1. Department of Biomedical
More informationTask-based Assessment of X-ray Breast Imaging Systems Using Insilico
55 th : Virtual Tools for Validation of X-ray Breast Imaging Systems Task-based Assessment of X-ray Breast Imaging Systems Using Insilico Modeling Tools Rongping Zeng Division of Imaging and Applied Mathematics
More informationCT imaging using energy-sensitive photon-counting detectors. Disclosure. Vision 20/20 paper
CT imaging using energy-sensitive photon-counting detectors Katsuyuki Ken Taguchi, Ph.D. ktaguchi@jhmi.edu Division of Medical Imaging Physics Department of Radiology Johns Hopkins University No financial
More informationCT vs. VolumeScope: image quality and dose comparison
CT vs. VolumeScope: image quality and dose comparison V.N. Vasiliev *a, A.F. Gamaliy **b, M.Yu. Zaytsev b, K.V. Zaytseva ***b a Russian Sci. Center of Roentgenology & Radiology, 86, Profsoyuznaya, Moscow,
More informationContrast Enhancement with Dual Energy CT for the Assessment of Atherosclerosis
Contrast Enhancement with Dual Energy CT for the Assessment of Atherosclerosis Stefan C. Saur 1, Hatem Alkadhi 2, Luca Regazzoni 1, Simon Eugster 1, Gábor Székely 1, Philippe Cattin 1,3 1 Computer Vision
More informationNon-Stationary CT Image Noise Spectrum Analysis
Non-Stationary CT Image Noise Spectrum Analysis Michael Balda, Björn J. Heismann,, Joachim Hornegger Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen Siemens Healthcare, Erlangen michael.balda@informatik.uni-erlangen.de
More informationSpatial Resolution Properties in Penalized-Likelihood Reconstruction of Blurred Tomographic Data
Spatial Resolution Properties in Penalized-Likelihood Reconstruction of Blurred Tomographic Data Wenying Wang, Grace J. Gang and J. Webster Stayman Department of Biomedical Engineering, Johns Hopkins University,
More informationDeviceless respiratory motion correction in PET imaging exploring the potential of novel data driven strategies
g Deviceless respiratory motion correction in PET imaging exploring the potential of novel data driven strategies Presented by Adam Kesner, Ph.D., DABR Assistant Professor, Division of Radiological Sciences,
More informationBackground 8/2/2011. Development of Breast Models for Use in Simulation of Breast Tomosynthesis and CT Breast Imaging. Stephen J.
Development of Breast Models for Use in Simulation of Breast Tomosynthesis and CT Breast Imaging Stephen J. Glick* J. Michael O Connor**, Clay Didier**, Mini Das*, * University of Massachusetts Medical
More informationUnique Features of the GE Senoclaire Tomosynthesis System. Tyler Fisher, M.S., DABR Therapy Physics, Inc.
Unique Features of the GE Senoclaire Tomosynthesis System Tyler Fisher, M.S., DABR Therapy Physics, Inc. Conflict of Interest Disclosure I have no conflicts to disclose. Learning Objectives Overview of
More information8/2/2017. Disclosure. Philips Healthcare (Cleveland, OH) provided the precommercial
8//0 AAPM0 Scientific Symposium: Emerging and New Generation PET: Instrumentation, Technology, Characteristics and Clinical Practice Aug Wednesday 0:4am :pm Solid State Digital Photon Counting PET/CT Instrumentation
More informationJames R Halama, PhD Loyola University Medical Center
James R Halama, PhD Loyola University Medical Center Conflicts of Interest Nuclear Medicine and PET physics reviewer for the ACR Accreditation program Learning Objectives Be familiar with the tests recommended
More informationDUAL energy X-ray radiography [1] can be used to separate
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 53, NO. 1, FEBRUARY 2006 133 A Scatter Correction Using Thickness Iteration in Dual-Energy Radiography S. K. Ahn, G. Cho, and H. Jeon Abstract In dual-energy
More informationLorad FFDM QC Procedures for Medical Physicist. Tao Wu, Ph.D. Hologic, Inc.
Lorad FFDM QC Procedures for Medical Physicist Tao Wu, Ph.D. Hologic, Inc. Lorad Selenia FFDM Tests Following Lorad QC Manual Collimation Assessment Artifact Evaluation System Resolution Phantom Image
More informationAcknowledgments. High Performance Cone-Beam CT of Acute Traumatic Brain Injury
A. Sisniega et al. (presented at RSNA 214) High Performance Cone-Beam CT of Acute Traumatic Brain Injury A. Sisniega 1 W. Zbijewski 1, H. Dang 1, J. Xu 1 J. W. Stayman 1, J. Yorkston 2, N. Aygun 3 V. Koliatsos
More informationPatient Specific Imaging Phantoms Using 3D Printing
Patient Specific Imaging Phantoms Using 3D Printing Shuai Leng, PhD Professor, Dept. of Radiology Mayo Clinic, Rochester MN, 55905 Phantom Imaging phantom, or simply phantom, is a specially designed object
More informationFrequency-based method to optimize the number of projections for industrial computed tomography
More Info at Open Access Database www.ndt.net/?id=18739 Frequency-based method to optimize the number of projections for industrial computed tomography Andrea Buratti 1, Soufian Ben Achour 1, Christopher
More informationRespiratory Motion Compensation for Simultaneous PET/MR Based on Strongly Undersampled Radial MR Data
Respiratory Motion Compensation for Simultaneous PET/MR Based on Strongly Undersampled Radial MR Data Christopher M Rank 1, Thorsten Heußer 1, Andreas Wetscherek 1, and Marc Kachelrieß 1 1 German Cancer
More informationMicro-CT Methodology Hasan Alsaid, PhD
Micro-CT Methodology Hasan Alsaid, PhD Preclinical & Translational Imaging LAS, PTS, GlaxoSmithKline 20 April 2015 Provide basic understanding of technical aspects of the micro-ct Statement: All procedures
More informationComputer-Tomography II: Image reconstruction and applications
Computer-Tomography II: Image reconstruction and applications Prof. Dr. U. Oelfke DKFZ Heidelberg Department of Medical Physics (E040) Im Neuenheimer Feld 280 69120 Heidelberg, Germany u.oelfke@dkfz.de
More informationMathematical methods and simulations tools useful in medical radiation physics
Mathematical methods and simulations tools useful in medical radiation physics Michael Ljungberg, professor Department of Medical Radiation Physics Lund University SE-221 85 Lund, Sweden Major topic 1:
More informationF3-A5: Model-Based Iterative Reconstruction for Advanced Baggage Screening
F3-A5: Model-Based Iterative Reconstruction for Advanced Baggage Screening Abstract While traditional direct reconstruction algorithms such as filtered back projection (FBP) and direct Fourier method (DFM)
More information7/31/ D Cone-Beam CT: Developments and Applications. Disclosure. Outline. I have received research funding from NIH and Varian Medical System.
4D Cone-Beam CT: Developments and Applications Lei Ren, PhD, DABR Department of Radiation Oncology Duke University Medical Center Disclosure I have received research funding from NIH and Varian Medical
More informationTomographic Reconstruction
Tomographic Reconstruction 3D Image Processing Torsten Möller Reading Gonzales + Woods, Chapter 5.11 2 Overview Physics History Reconstruction basic idea Radon transform Fourier-Slice theorem (Parallel-beam)
More informationEvaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging
Evaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging Nicholas Petrick, Hyun J. Grace Kim, David Clunie, Kristin
More informationF3-A5: Toward Model-Based Reconstruction in Scanned Baggage Security Applications
F3-A5: Toward Model-Based Reconstruction in Scanned Baggage Security Applications Abstract While traditional direct reconstruction algorithms such as filtered back projection depend on the analytic inversion
More informationComputed Tomography. Principles, Design, Artifacts, and Recent Advances. Jiang Hsieh THIRD EDITION. SPIE PRESS Bellingham, Washington USA
Computed Tomography Principles, Design, Artifacts, and Recent Advances THIRD EDITION Jiang Hsieh SPIE PRESS Bellingham, Washington USA Table of Contents Preface Nomenclature and Abbreviations xi xv 1 Introduction
More informationMotion Compensation from Short-Scan Data in Cardiac CT
Motion Compensation from Short-Scan Data in Cardiac CT Juliane Hahn 1,2, Thomas Allmendinger 1, Herbert Bruder 1, and Marc Kachelrieß 2 1 Siemens Healthcare GmbH, Forchheim, Germany 2 German Cancer Research
More informationImplementation and evaluation of a protocol management system for automated review of CT protocols
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, VOLUME 17, NUMBER 5, 2016 Implementation and evaluation of a protocol management system for automated review of CT protocols Joshua Grimes, Shuai Leng, a Yi
More informationGPU implementation for rapid iterative image reconstruction algorithm
GPU implementation for rapid iterative image reconstruction algorithm and its applications in nuclear medicine Jakub Pietrzak Krzysztof Kacperski Department of Medical Physics, Maria Skłodowska-Curie Memorial
More informationQIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD)
5 QIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD) Stage: Consensus+ Draft Tech Confirmed When referencing this document, please use the following format: QIBA CT Volumetry Technical
More informationCT Basics Principles of Spiral CT Dose. Always Thinking Ahead.
1 CT Basics Principles of Spiral CT Dose 2 Who invented CT? 1963 - Alan Cormack developed a mathematical method of reconstructing images from x-ray projections Sir Godfrey Hounsfield worked for the Central
More informationCharacterization of a Time-of-Flight PET Scanner based on Lanthanum Bromide
2005 IEEE Nuclear Science Symposium Conference Record M04-8 Characterization of a Time-of-Flight PET Scanner based on Lanthanum Bromide J. S. Karp, Senior Member, IEEE, A. Kuhn, Member, IEEE, A. E. Perkins,
More informationImage Quality Assessment and Quality Assurance of Advanced Imaging Systems for IGRT. AAPM Penn-Ohio Chapter Sep 25, 2015 Soyoung Lee, PhD
Image Quality Assessment and Quality Assurance of Advanced Imaging Systems for IGRT AAPM Penn-Ohio Chapter Sep 25, 2015 Soyoung Lee, PhD 1 Outline q Introduction q Imaging performances in 4D-CBCT Image
More informationMEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY. Prof. Yasser Mostafa Kadah
MEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY Prof. Yasser Mostafa Kadah www.k-space.org Recommended Textbook X-Ray Computed Tomography in Biomedical Engineering, by Robert Cierniak, Springer, 211 Computed Tomography
More informationQIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD)
5 QIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD) Stage 3: Technically Confirmed June 22, 2018 When referencing this document, please use the following format: QIBA CT Volumetry Technical
More informationLimited view X-ray CT for dimensional analysis
Limited view X-ray CT for dimensional analysis G. A. JONES ( GLENN.JONES@IMPERIAL.AC.UK ) P. HUTHWAITE ( P.HUTHWAITE@IMPERIAL.AC.UK ) NON-DESTRUCTIVE EVALUATION GROUP 1 Outline of talk Industrial X-ray
More informationProjection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT
Projection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT Benedikt Lorch 1, Martin Berger 1,2, Joachim Hornegger 1,2, Andreas Maier 1,2 1 Pattern Recognition Lab, FAU Erlangen-Nürnberg
More informationC a t p h a n / T h e P h a n t o m L a b o r a t o r y
C a t p h a n 5 0 0 / 6 0 0 T h e P h a n t o m L a b o r a t o r y C a t p h a n 5 0 0 / 6 0 0 Internationally recognized for measuring the maximum obtainable performance of axial, spiral and multi-slice
More informationComputed Tomography. What a progress
IAEA RER/9/135 COURSE ON OPTIMIZATION IN COMPUTED TOMOGRAPHY Sofia, Bulgaria, 2017 Computed Tomography (a little bit of all) Dean Pekarovič UMC Ljubljana, Institute of Radiology Quality and Safety office
More informationLearning Objectives. Outline. Lung Cancer Workshop VIII 8/2/2012. Nicholas Petrick 1. Methodologies for Evaluation of Effects of CAD On Users
Methodologies for Evaluation of Effects of CAD On Users Nicholas Petrick Center for Devices and Radiological Health, U.S. Food and Drug Administration AAPM - Computer Aided Detection in Diagnostic Imaging
More informationA closer look at CT scanning
Vet Times The website for the veterinary profession https://www.vettimes.co.uk A closer look at CT scanning Author : Charissa Lee, Natalie Webster Categories : General, Vets Date : April 3, 2017 A basic
More informationHot Topics in Imaging Physics A Key Interdisciplinary Science for 21 st Century Medicine
Hot Topics in Imaging Physics A Key Interdisciplinary Science for 21 st Century Medicine Jeff Siewerdsen, PhD FAAPM Department of Biomedical Engineering Department of Computer Science Russell H. Morgan
More informationTomoTherapy Related Projects. An image guidance alternative on Tomo Low dose MVCT reconstruction Patient Quality Assurance using Sinogram
TomoTherapy Related Projects An image guidance alternative on Tomo Low dose MVCT reconstruction Patient Quality Assurance using Sinogram Development of A Novel Image Guidance Alternative for Patient Localization
More informationQIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD)
5 QIBA Profile: CT Tumor Volume Change for Advanced Disease (CTV-AD) Stage: Consensus When referencing this document, please use the following format: QIBA CT Volumetry Technical Committee. CT Tumor Volume
More informationRADIOMICS: potential role in the clinics and challenges
27 giugno 2018 Dipartimento di Fisica Università degli Studi di Milano RADIOMICS: potential role in the clinics and challenges Dr. Francesca Botta Medical Physicist Istituto Europeo di Oncologia (Milano)
More informationDUE to beam polychromacity in CT and the energy dependence
1 Empirical Water Precorrection for Cone-Beam Computed Tomography Katia Sourbelle, Marc Kachelrieß, Member, IEEE, and Willi A. Kalender Abstract We propose an algorithm to correct for the cupping artifact
More informationDUAL-ENERGY CT IN PROTON THERAPY
10/31/17 DUAL-ENERGY CT IN PROTON THERAPY Isabel Almeida, MAASTRO Clinic 7th NCS Lustrum Symposium 1 10/31/17 http://zonptc.bouwwebcam.nl https://www.youtube.com/watch?v=3vvvf5bqn7g Range uncertainties
More informationReview of PET Physics. Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA
Review of PET Physics Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA Chart of Nuclides Z (protons) N (number of neutrons) Nuclear Data Evaluation Lab.
More informationClassification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging
1 CS 9 Final Project Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging Feiyu Chen Department of Electrical Engineering ABSTRACT Subject motion is a significant
More information7/11/2015. Imaging Equipment Specification and Selection in Radiation Oncology Department. What Therapy Physicists Need to Know
Imaging Equipment Specification and Selection in Radiation Oncology Department Chiaho Hua St. Jude Children s Research Hospital Yue Cao University of Michigan, Ann Arbor Margie Hunt Memorial Sloan Kettering
More informationCorrelated Polarity Noise Reduction: Development, Analysis, and Application of a Novel Noise Reduction Paradigm. Jered R Wells
Correlated Polarity Noise Reduction: Development, Analysis, and Application of a Novel Noise Reduction Paradigm By Jered R Wells Graduate Program in Medical Physics Duke University Date: Approved: James
More informationPractical considerations for noise power spectra estimation for clinical CT scanners
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2016 Practical considerations for noise power spectra estimation for clinical CT scanners Steven Dolly Washington
More informationTechnical Note: Model-based magnification/minification correction of patient size surrogates extracted from CT localizers
Technical Note: Model-based magnification/minification correction of patient size surrogates extracted from CT localizers Christiane Sarah Burton Department of Radiology, University of Wisconsin-Madison,
More informationMonte Carlo methods in proton beam radiation therapy. Harald Paganetti
Monte Carlo methods in proton beam radiation therapy Harald Paganetti Introduction: Proton Physics Electromagnetic energy loss of protons Distal distribution Dose [%] 120 100 80 60 40 p e p Ionization
More informationReconstruction of CT Images from Sparse-View Polyenergetic Data Using Total Variation Minimization
1 Reconstruction of CT Images from Sparse-View Polyenergetic Data Using Total Variation Minimization T. Humphries and A. Faridani Abstract Recent work in CT image reconstruction has seen increasing interest
More informationPenalized-Likelihood Reconstruction for Sparse Data Acquisitions with Unregistered Prior Images and Compressed Sensing Penalties
Penalized-Likelihood Reconstruction for Sparse Data Acquisitions with Unregistered Prior Images and Compressed Sensing Penalties J. W. Stayman* a, W. Zbijewski a, Y. Otake b, A. Uneri b, S. Schafer a,
More informationRECENTLY APPROVED GE FFDM DBT TESTING PROCEDURES.
RECENTLY APPROVED GE FFDM DBT TESTING PROCEDURES. S. G U R U P R A S A D, P H. D., D A B R, E M E R I T U S, M AT H E W H A L L M. S. N O RT H S H O R E U N I V E R S I T Y H E A LT H S Y S T E M A N D
More informationOutline. Outline 7/24/2014. Fast, near real-time, Monte Carlo dose calculations using GPU. Xun Jia Ph.D. GPU Monte Carlo. Clinical Applications
Fast, near real-time, Monte Carlo dose calculations using GPU Xun Jia Ph.D. xun.jia@utsouthwestern.edu Outline GPU Monte Carlo Clinical Applications Conclusions 2 Outline GPU Monte Carlo Clinical Applications
More informationML reconstruction for CT
ML reconstruction for CT derivation of MLTR rigid motion correction resolution modeling polychromatic ML model dual energy ML model Bruno De Man, Katrien Van Slambrouck, Maarten Depypere, Frederik Maes,
More informationSimulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images
Simulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images Tao Han, Chris C. Shaw, Lingyun Chen, Chao-jen Lai, Xinming Liu, Tianpeng Wang Digital Imaging Research Laboratory (DIRL),
More informationThe team. Disclosures. Ultrasound Guidance During Radiation Delivery: Confronting the Treatment Interference Challenge.
Ultrasound Guidance During Radiation Delivery: Confronting the Treatment Interference Challenge Dimitre Hristov Radiation Oncology Stanford University The team Renhui Gong 1 Magdalena Bazalova-Carter 1
More informationDIPLOMA THESIS. Optimization in CT. Evaluation of dose saving potential in a thorax-abdomen/pelvis protocol using iterative reconstruction techniques
Optimization in CT Evaluation of dose saving potential in a thorax-abdomen/pelvis protocol using iterative reconstruction techniques Ingvild Dalehaug Master of Science in Physics and Mathematics Submission
More informationRADIOLOGY AND DIAGNOSTIC IMAGING
Day 2 part 2 RADIOLOGY AND DIAGNOSTIC IMAGING Dr hab. Zbigniew Serafin, MD, PhD serafin@cm.umk.pl 2 3 4 5 CT technique CT technique 6 CT system Kanal K: RSNA/AAPM web module: CT Systems & CT Image Quality
More informationHenry Ford NERS/BIOE 481. Lecture 11 B Computed Tomography (CT)
NERS/BIOE 481 Lecture 11 B Computed Tomography (CT) Michael Flynn, Adjunct Prof Nuclear Engr & Rad. Science mikef@umich.edu mikef@rad.hfh.edu Henry Ford Health System RADIOLOGY RESEARCH VII Computed Tomography
More informationEmpirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography
Empirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography Marc Kachelrieß, a Katia Sourbelle, and Willi A. Kalender Institute of Medical Physics, University of
More information