(Ebook PDF) Electrical Impedance Tomography Methods History and Applications 2nd Edition by David Holder 0429680872 9780429680878 full chapters

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ISBN-10 ‏ : ‎0429680872 

ISBN-13 ‏ : ‎9780429680878

Author :  David Holder 

With contributions from leading international researchers, this second edition of Electrical Impedance Tomography: Methods, History and Applications has been fully updated throughout and contains new developments in the field, including sections on image interpretation and image reconstruction. Providing a thorough review of the progress of EIT, the present state of knowledge, and a look at future advances and applications, this accessible reference will be invaluable for mathematicians, physicists dealing with bioimpedance, electronic engineers involved in developing and extending its applications, and clinicians wishing to take advantage of this powerful imaging method. Key Features: Fully updated throughout, with new sections on image interpretation and image reconstruction Overview of the current state of experimental and clinical use of EIT as well as active research developments Overview of related research in geophysics, industrial process tomography, magnetic-resonance and magnetic-induction impedance imaging

Electrical Impedance Tomography; Methods, History and Applications 2nd Table of contents:

SECTION I Section: Introduction
Chapter 1 Electrical Impedance Tomography
1.1 Introduction
1.2 Overview
1.3 EIT Image Generation and Interpretation
1.3.1 Tissue Electrical Properties
1.3.2 EIT Electronics
1.3.3 Models of Sensitivity
1.3.4 EIT Image Reconstruction
1.3.5 Image Interpretation
1.4 EIT Applications and Perspectives
Chapter 2 Introduction to EIT Concepts and Technology
2.1 Biomedical Electrical Impedance Tomography
2.2 Brief Introduction to Bioimpedance
2.2.1 Resistance and Capacitance
2.2.2 Impedance in Biological Tissue
2.2.3 Other Related Measures of Impedance
2.2.4 Impedance Measurement
2.2.5 Relevance to Electrical Impedance Tomography
2.3 Introduction to Biomedical EIT
2.3.1 Historical Perspective
2.3.2 EIT Instrumentation
2.3.2.1 Individual Impedance Measurements
2.3.2.2 Data Collection
2.3.3 Electrodes
2.3.4 Setting Up and Calibrating Measurements
2.3.5 Data Collection Strategies
2.3.6 EIT Image Reconstruction
2.3.6.1 Backprojection
2.3.6.2 Sensitivity Matrix Approaches
2.3.6.3 Other Developments in Algorithms
2.3.7 Current Developments
SECTION II EIT: Tissue Properties to Image Measures/Chapter
Chapter 3 Electromagnetic Properties of Tissues
3.1 What Underlies Tissue Electromagnetic Properties?
3.1.1 Ionic Conductivities
3.1.2 Membranes and Solid Tissues
3.1.3 Relaxation Models of Tissue Properties
3.2 Overall Tissue Conductivities
3.2.1 Properties of Fluids, Cell Suspensions and Blood
3.2.1.1 Cerebrospinal Fluid
3.2.1.2 Blood
3.2.2 Bone
3.2.3 Liver
3.2.4 Lung
3.2.5 Pathology
3.2.5.1 Properties of Tumor Tissues
3.2.5.2 Lung Injury
3.2.5.3 Ischemia
3.2.6 Active Membrane Properties
3.3 Measurement of Impedance Properties
3.3.1 Electrode Properties
3.3.2 Conductivity Cell and Dependence on Geometry
3.3.3 High Frequency (>50 MHz) Properties
3.4 Tissue Anisotropy
3.5 Electrical Safety and Current Limitations
3.6 Conclusions and Perspective
Chapter 4 Electronics and Hardware
4.1 Hardware Challenges and Approaches
4.1.1 Speed and Precision
4.1.2 Applied Currents vs. Voltages
4.1.3 Pair-Drive vs. Parallel-Drive Systems
4.1.4 Voltage Measurement on Current-Carrying Electrodes
4.2 Electrode Excitation
4.2.1 Current Sources
4.2.1.1 Floating and Single-Ended Current Sources
4.2.1.2 Current Source Requirements
4.2.1.3 Stray Capacitance
4.2.1.4 Current Source Compensation
4.2.1.5 Current Source and Compensation Circuits
4.2.1.6 Capacitance Mitigation Circuits
4.2.2 Voltage Sources
4.2.3 Connecting to Electrodes
4.2.3.1 Cables
4.2.3.2 Active Electrodes
4.2.4 Multiplexers vs. Parallel Hardware
4.3 Voltage Measurement
4.3.1 Matched Filter
4.3.1.1 Noise
4.3.2 Differential vs. Single-Ended Voltage Measurement
4.3.3 Common-mode Voltage Feedback
4.4 EIT Systems
4.5 Conclusion
Chapter 5 The EIT Forward Problem
5.1 Introduction
5.2 Mathematical Setting
5.2.1 Quasi-static Approximation
5.3 Current Propagation in Conductive Bodies
5.3.1 Analytical Solutions
5.3.2 Circular Anomaly in a Unit Disk
5.3.3 Detectability
5.4 Measurements and Electrodes
5.5 Measurement Strategy
5.5.1 Linear Regression
5.5.2 Adjacent Measurement Protocol
5.5.3 Optimal Drive Patterns
5.6 EIT Sensitivity
5.6.1 Standard Formula for Jacobian
5.7 Solving the Forward Problem: The Finite Element Method
5.7.1 Basic FEM Formulation
5.7.2 Solving the Linear System
5.7.3 Conjugate Gradient and Krylov Subspace Methods
5.7.4 Mesh Generation
5.8 Further Comments on the Forward Model
Chapter 6 The EIT Inverse Problem
6.1 Introduction
6.2 Why is EIT So Hard?
6.3 Inverse Problem
6.4 Regularizing Linear Ill-posed Problems
6.4.1 Ill-conditioning
6.4.2 Tikhonov Regularization
6.4.3 The Singular Value Decomposition
6.4.4 Studying Ill-conditioning with the SVD
6.4.5 More General Regularization
6.5 Regularizing EIT
6.6 Difference EIT
6.6.1 Linearized EIT Reconstruction
6.6.2 Selection of Hyperparameter, α
6.6.3 Regularization Parameters
6.6.4 Backprojection
6.6.5 GREIT
6.7 Absolute EIT
6.7.1 Iterative Nonlinear Solution
6.8 Total Variation Regularization
6.9 Common Pitfalls and Best Practice
6.10 Further Developments in Reconstruction Algorithms
6.10.1 Beyond Tikhonov regularization
6.10.2 Direct Non-linear Methods
6.11 Machine Learning and Inverse Problems
6.12 Practical Applications
Chapter 7 D-bar Methods for EIT
7.1 Introduction
7.2 Calderón’s Method
7.3 The Rise of the CGO Solutions
7.4 A 2-D D-bar Method
7.4.1 Equations of the D-bar Method
7.4.2 Numerical Solution of the Equations
7.4.2.1 Computing the DN Map from Measured Data
7.4.2.2 Computation of the Scattering Transform
7.4.2.3 Numerical Solution of the D-bar Equation
7.4.3 Examples of Reconstructions
7.5 New and Recent Directions in D-bar Methods
Chapter 8 EIT Image Interpretation
8.1 Elements of EIT Images
8.1.1 Valid Size of an Image
8.1.2 Colour Mapping and Colour Scale
8.1.3 Sampling Frequency and Mixture of Signals
8.1.4 Meaning of the Pixels in Different Types of Images
8.2 Functional Images and EIT Measures
8.2.1 Simple Distribution of the Impedance Changes in Certain ROIs
8.2.1.1 Example: Averaging Tidal Variation fEIT
8.2.1.2 Example: Regression fEIT
8.2.1.3 Example: Cardiac-related fEIT
8.2.1.4 Example: Identifying and Tracking of Intracranial Resistivity Changes
8.2.2 Differences of Impedance Variation Calculated in Spatial Correlations
8.2.2.1 Example: Center of Ventilation (CoV)
8.2.2.2 Example: The Global Inhomogeneity Index (GI)
8.2.2.3 Example: Spatial Related Classification of Intracranial Resistivity Changes
8.2.3 Subtracting Temporal Information (Taking Advantages of the High Sampling Rate)
8.2.3.1 Example: Intra-tidal Volume Distribution (ITVD)
8.2.3.2 Example: Regional Ventilation Delay RVD, both fEIT and Index Available
8.2.4 New Units or Dimensions Deriving from Impedance
8.2.4.1 Example: Regional Compliance as an fEIT and the Application in PEEP Titration
8.2.4.2 Example: Regional Findings for Pulmonary Function Test
8.2.4.3 Example: Determining Impedance Measures Using Contrast Agents
8.2.4.4 Example: The Linear Correlation Metric Images for Frequency Difference EIT
8.3 Clinical Applications
8.3.1 Using Existing fEIT and Measures
8.3.2 Recommendations for Development of fEIT Images and Measures
8.4 List of fEIT Image and Measures
SECTION III Applications
Chapter 9 EIT for Measurement of Lung Function
9.1 Introduction
9.1.1 Basics of Lung Physiology and Pathophysiology
9.1.2 Lung-related Applications of EIT
9.2 EIT Examinations During Spontaneous Quiet Tidal Breathing
9.2.1 Analysis of EIT Data Acquired During Spontaneous Quiet Tidal Breathing
9.2.2 Findings of EIT Studies Performed in Human Subjects During Quiet Tidal Breathing
9.3 EIT Examinations During Ventilation Manoeuvres and Pulmonary Function Testing
9.3.1 Analysis of EIT Data Acquired During Ventilation Manoeuvres and Pulmonary Function Testing
9.3.2 Findings of EIT Studies Performed in Human Subjects During Ventilation Manoeuvres and Pulmonary Function Testing
9.4 Summary
Chapter 10 EIT for Monitoring of Ventilation
10.1 Introduction
10.2 Assessment of Ventilation Distribution with EIT
10.3 Measures of Ventilation Inhomogeneity
10.4 Intratidal Ventilation Inhomogeneity and Alveolar Cycling
10.5 Identification of Overdistension and Alveolar Collapse at the Bedside Using Regional Compliance Estimation
10.6 Identification of Poorly Ventilated Lung Areas
10.7 End-expiratory Lung Impedance Changes for Quantification of Lung Recruitment and Derecruitment
10.8 Expiratory Time Constants for Monitoring Airflow Limitation
10.9 Comparison of Different Approaches for Optimizing Mechanical Ventilation with EIT
10.9.1 Gravity-dependent Ventilation Distribution
10.9.2 Global Inhomogeneity Index and Coefficient of Variation
10.9.3 Intratidal Ventilation Inhomogeneity
10.9.4 Quantification of Alveolar Overdistension and Collapse During a Decremental PEEP Trial (“Costa-Approach”)
10.9.5 Assessment of Changes in Regional Compliance with Different VT or PEEP Level
10.9.6 Poorly Ventilated Lung Areas (“Silent Spaces”)
10.9.7 Analyzing Changes in End-expiratory Lung Impedance
10.9.8 Regional Expiratory Time Constants
10.10 Conclusion
Chapter 11 EIT Monitoring of Hemodynamics
11.1 Introduction
11.2 Classical Methods and Key Parameters of Hemodynamic Measurements
11.2.1 Intra-Vascular Pressure Measurement
11.2.1.1 Systemic Arterial Pressure
11.2.1.2 Pulmonary Arterial Pressure
11.2.1.3 Systemic Venous Pressure
11.2.1.4 Pulmonary Venous Pressure
11.2.2 Flow Parameters
11.2.3 Volume Status Parameters
11.2.3.1 Stroke Volume Variation
11.2.3.2 Extravascular Lung Water
11.3 Origins of Cardiosynchronous Signals in EIT Measurements
11.4 Interfering Signals in Hemodynamic EIT Measurements
11.4.1 Blood Flow and Blood Volume Changes
11.4.2 Ventilation
11.4.2.1 Regions of Interest
11.4.2.2 ECG-Gating
11.4.2.3 Decomposition of Signals
11.4.2.4 Apnea Measurements
11.4.3 Heart Movement
11.5 EIT Measurements for Hemodynamics
11.5.1 Intra-Arterial Pressure Measurement by Pulse Transit Time
11.5.1.1 Determining Pulse Arrival Times with EIT
11.5.1.2 Aortic Blood Pressure
11.5.1.3 Pulmonary Artery Pressure
11.5.2 Flow Parameters
11.5.2.1 Pulmonary Perfusion
11.5.2.2 Regional V˙/Q Matching
11.5.2.3 Cardiac Output and Stroke Volume
11.5.3 Volume Status
11.5.3.1 Stroke Volume Variation and Heart-Lung-Interaction
11.5.3.2 Detection of Aortic ROI
11.5.3.3 Extravascular Lung Water
11.6 Summary and Outlook
Chapter 12 EIT Imaging of Brain and Nerves
12.1 Introduction
12.2 Physiological Basis of EIT of Brain Function
12.2.1 Bioimpedance of Brain and Nerve and Changes During Activity or Pathological Conditions
12.2.1.1 Impedance of Resting Brain
12.2.1.2 Anoxic Depolarization and Cerebral Ischemia
12.2.1.3 Slow Impedance Changes During Functional Activity
12.2.1.4 Functional Activity with the Time Course of the Action Potential
12.2.2 Other Mechanisms of Impedance Change: Temperature and CSF Movements
12.2.3 Effect of Coverings of the Brain When Recording EIT with Scalp Electrodes
12.3 EIT Systems Developed for Brain Imaging
12.3.1 Hardware
12.3.1.1 Relevant Instrumentation Principles
12.3.1.2 EIT Systems Developed in the UCL Group
12.3.2 Reconstruction Algorithms for EIT of Brain Function
12.3.2.1 Reconstruction Algorithms for Time Difference EIT Imaging Based on a Linear Assumption
12.3.2.2 Non-linear Reconstruction Algorithms for EIT of Brain Function
12.3.3 Development of Tanks for Testing of EIT Systems
12.4 EIT of Slow Impedance Changes in the Brain Related to Changes in Blood Volume and Cell Swelling
12.4.1 During Physiological Evoked Activity
12.4.2 EIT of Slow Changes During Epileptic Seizures
12.4.2.1 Proof of Concept in Animal Studies
12.4.2.2 Human Studies
12.5 EIT in Cerebral Pathology Over Hours or Days
12.5.1 Time Difference Cerebral EIT Over Hours or Days
12.5.2 Multifrequency EIT in Acute Stroke
12.6 EIT of Neuronal Depolarization
12.6.1 Fast Neural in the Brain During Evoked Physiological Activity and Epileptic Seizures
12.6.2 Fast Neural EIT in Peripheral Nerve
12.7 Conclusion
Chapter 13 EIT for Imaging of Cancer
13.1 EIS in Cancer
13.1.1 Cancer Biology and Impedance Contrast Mechanism
13.1.2 Anatomy-specific Impedance Contrast
13.1.2.1 Breast
13.1.2.2 Prostate
13.2 Breast EIT
13.2.1 Introduction
13.2.2 Other Methods in Use for Breast Cancer Detection
13.2.3 Different Approaches to Breast EIT
13.2.4 Impedance Mapping
13.2.5 Tomographic Imaging
13.2.6 Limitations of Impedance Measurements
13.2.7 Advantages of Impedance as a Screening Tool
13.2.8 Clinical Results Summaries
13.2.9 Planar Geometry Systems
13.2.9.1 Piperno 1990 [830]13.2.9.2 Malich 2000 [675]13.2.9.3 Cherepenin 2001 [194]13.2.9.4 Malich 2001a [674]13.2.9.5 Malich 2001b [673]13.2.9.6 Cherepin 2002 [196]13.2.9.7 Glickman 2002 [351]13.2.9.8 Martin 2002 [688]13.2.9.9 Stojadinovic 2005 [1007]13.2.9.10 Stojadinovic 2006 [1006]13.2.9.11 Trokhanova 2008 [1055]13.2.9.12 Raneta 2013 [862]13.2.10 Circular Geometry Systems
13.2.10.1 Osterman 2000 [796]13.2.10.2 Halter 2004, 2008c [405,406]13.2.10.3 Soni 2004 [997]13.2.10.4 Poplack 2004 [839]13.2.10.5 Poplack 2007 [840]13.2.10.6 Halter 2008c [406]13.2.10.7 Halter 2015 [408]13.2.10.8 Discussion of the Clinical Trials
13.3 Prostate EIT
13.3.1 Introduction
13.3.2 Transrectal EIT for Cancer Detection and Biopsy Guidance
13.3.3 Surgical Margin Assessment Using Endoscopic Electrode Arrays
13.4 Other Cancer EIT
13.5 Perspective on Cancer Imaging with EIT
Chapter 14 Other Clinical Applications of EIT
14.1 Tumour Ablation Monitoring
14.2 System-on-chip and Cell/tissue Imaging
14.3 Wearables
14.4 Intra-Pelvic Venous Congestion
14.5 Other Potential Applications
Chapter 15 Veterinary Applications of EIT
15.1 Introduction to Thoracic EIT in Veterinary Applications
15.1.1 Creation of Finite Element Models for Animals
15.2 Translational Research in Animals
15.2.1 Pig
15.2.2 Dog
15.2.3 Lamb and Sheep
15.2.4 Horse
15.3 Clinical Research in Animals
15.3.1 Horses
15.3.2 Dog
15.3.3 Rhinoceros
15.4 Clinical Applications in Animals
15.5 Future of EIT in Veterinary Applications
SECTION IV Related Technologies
Chapter 16 Magnetic Induction Tomography
16.1 Introduction
16.2 The MIT Signal
16.3 MIT Array Design
16.3.1 Excitors and Sensors
16.3.2 Array Configuration
16.3.3 Screening
16.3.4 Cancellation of the Primary Signal
16.4 Signal Demodulation
16.5 Working Imaging Systems and Proposed Applications
16.5.1 MIT for the Process Industry
16.5.2 Security Applications
16.5.3 Petrochemical Industry
16.5.4 Biomedical MIT
16.6 Image Reconstruction
16.7 Spatial Resolution, Conductivity Resolution and Noise
16.8 Propagation Delays
16.9 Scaling the Size of an MIT Array
16.10 Multifrequency Measurements: Magnetic Induction Spectroscopy
16.11 Imaging Permittivity and Permeability
16.12 Conclusions
Chapter 17 Electrical Impedance Imaging Using MRI
17.1 Introduction
17.2 Conductivity, Permittivity, and Maxwell’s Equations
17.3 Magnetic Resonance Electrical Impedance Tomography (MREIT)
17.3.1 Governing Equations in MREIT
17.3.2 Measurement Techniques in MREIT
17.3.3 Image Reconstruction Algorithms in MREIT
17.3.3.1 Harmonic Bz Algorithm
17.3.3.2 Projected Current Density Algorithm
17.3.3.3 Direct Harmonic Bz Algorithm
17.3.4 Clinical Applications of MREIT
17.3.4.1 MREIT for Transcranial Direct Current Stimulation (tDCS)
17.3.4.2 MREIT for Deep Brain Stimulation (DBS)
17.3.5 Future Work in MREIT
17.4 Magnetic Resonance Electrical Properties Tomography (MREPT)
17.4.1 Governing Equation in MREPT
17.4.2 Measurement Techniques in MREPT
17.4.3 Image Reconstruction Algorithms in MREPT
17.4.4 Clinical Applications of MREPT
17.4.4.1 Imaging of Conductivity Changes Caused by Radiation Therapy (RT)
17.4.4.2 Conductivity Imaging of Ischemic Stroke
17.4.4.3 Conductivity Imaging of Lower Extremity
17.4.5 Future Work in MREPT
17.5 Frequency Dependence and Direction Dependence of Conductivity
17.5.1 Frequency Dependence of Conductivity
17.5.2 Direction Dependence of Conductivity
17.5.3 Conductivity Tensor and Water Diffusion Tensor
17.6 Diffusion Tensor Magnetic Resonance Electrical Impedance Tomography (DT-MREIT)
17.6.1 Governing Equations in DT-MREIT
17.6.2 Measurement Techniques in DT-MREIT
17.6.3 Image Reconstruction Algorithms in DT-MREIT
17.6.4 DT-MREIT Imaging Experiments
17.7 Conductivity Tensor Imaging (CTI)
17.7.1 Governing Equations of CTI
17.7.2 Measurement Techniques
17.7.3 Image Reconstruction Algorithms in CTI
17.7.3.1 Extraction of dew, diw and α
17.7.3.2 Estimation of Dew
17.7.3.3 Estimation of β
17.7.4 CTI Imaging Experiments
17.7.4.1 Conductivity Phantom with Giant Vesicle Suspension
17.7.4.2 In Vivo Human Brain
17.7.5 Clinical Applications of CTI
17.7.6 Future Work in CTI
17.8 Summary
Chapter 18 Geophysical ERT
18.1 Introduction
18.2 Common Applications
18.3 Research Applications
18.4 Complex Resistivity and Induced Polarization
18.5 Logarithmic Parametrization
18.6 Absolute Reconstruction
18.7 Timelapse Inversion
18.8 The Use of Electrode Models
18.9 Modelling Open Domains
18.10 2.5D Calculations
18.11 Data Quality Measures
18.12 Data Weighting
18.13 Available Hardware
18.14 Available Software
18.15 Discussion
Chapter 19 Industrial Process Tomography
19.1 Introduction
19.2 Data Acquisition
19.2.1 Electrical Resistance Tomography (ERT)
19.2.2 Electrical Capacitance Tomography (ECT)
19.2.3 Magnetic Induction Tomography (MIT)
19.2.4 Electrical Impedance Tomography
19.2.5 Intrinsically Safe Systems
19.3 Previous Industrial Applications of Electrical Tomography
19.3.1 Applications of Electrical Resistance Tomography Technology to Pharmaceutical Processes
19.3.2 Imaging the Flow Profile of Molten Steel Through a Submerged Pouring Nozzle
19.3.3 The Application of Electrical Resistance Tomography to a Large Volume Production Pressure Filter
19.3.4 A Novel Tomographic Flow Analysis System
19.3.5 Application of Electrical Capacitance Tomography for Measurement of Gas/solids Flow Characteristics in a Pneumatic Conveying System
19.4 Recent Industrial Applications of Electrical Tomography
19.4.1 Application of Electrical Resistance Tomography to the Measurement of Batch Mixing
19.4.2 Application of Electrical Resistance Tomography to Inline Flow Measurement
19.4.3 Application of Electrical Resistance Tomography to Cleaning-in-Place
19.5 Summary
Chapter 20 Devices, History and Conferences
20.1 EIT Conferences
20.2 Historical Perspective
20.3 EIT Hardware
20.3.1 Commercial Systems
20.3.1.1 Draeger Medical
20.3.1.2 Sentec
20.3.1.3 Sciospec
20.3.2 Research Systems
20.3.2.1 Sheffield Mk 2 System
20.3.2.2 Goettingen GoeMFII System
20.3.2.3 cole Polytechnique de Montr
20.3.2.4 Russian Academy of Sciences Breast Imaging System
20.3.2.5 CRADL System
20.3.2.6 Rensselaer Polytechnic Institute ACT 3 System
20.3.2.7 KHU Mark2.5 System
20.3.2.8 Dartmouth Broadband, High Frequency System

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