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E-grāmata: Electrical Impedance Tomography: Methods, History and Applications 2nd edition [Taylor & Francis e-book]

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  • Formāts: 518 pages, 17 Tables, black and white; 157 Line drawings, black and white; 49 Halftones, black and white; 206 Illustrations, black and white
  • Sērija : Series in Medical Physics and Biomedical Engineering
  • Izdošanas datums: 20-Dec-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9780429399886
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Electrical Impedance Tomography: Methods, History and Applications 2nd editionPalielināt
  • Formāts: 518 pages, 17 Tables, black and white; 157 Line drawings, black and white; 49 Halftones, black and white; 206 Illustrations, black and white
  • Sērija : Series in Medical Physics and Biomedical Engineering
  • Izdošanas datums: 20-Dec-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9780429399886
Citas grāmatas par šo tēmu:
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"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 engineerers 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"--

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.

Section I Section: Introduction
Chapter 1 Electrical Impedance Tomography
3(10)
Andy Adler
1.1 Introduction
3(1)
1.2 Overview
4(1)
1.3 EIT Image Generation and Interpretation
5(6)
1.3.1 Tissue Electrical Properties
6(1)
1.3.2 EIT Electronics
7(1)
1.3.3 Models of Sensitivity
8(1)
1.3.4 EIT Image Reconstruction
9(1)
1.3.5 Image Interpretation
10(1)
1.4 EIT Applications and Perspectives
11(2)
Chapter 2 Introduction to EIT Concepts and Technology
13(20)
David Holder
2.1 Biomedical Electrical Impedance Tomography
13(1)
2.2 Brief Introduction to Bioimpedance
14(6)
2.2.1 Resistance and Capacitance
14(2)
2.2.2 Impedance in Biological Tissue
16(2)
2.2.3 Other Related Measures of Impedance
18(1)
2.2.4 Impedance Measurement
19(1)
2.2.5 Relevance to Electrical Impedance Tomography
20(1)
2.3 Introduction to Biomedical EIT
20(14)
2.3.1 Historical Perspective
20(1)
2.3.2 EIT Instrumentation
21(1)
2.3.2.1 Individual Impedance Measurements
21(1)
2.3.2.2 Data Collection
22(2)
2.3.3 Electrodes
24(1)
2.3.4 Setting Up and Calibrating Measurements
24(1)
2.3.5 Data Collection Strategies
25(1)
2.3.6 EIT Image Reconstruction
26(1)
2.3.6.1 Backprojection
26(1)
2.3.6.2 Sensitivity Matrix Approaches
26(1)
2.3.6.3 Other Developments in Algorithms
28(1)
2.3.7 Current Developments
29(4)
Section II EIT: Tissue Properties to Image Measures/Chapter
Chapter 3 Electromagnetic Properties of Tissues
33(20)
Rosalind Sadleir
Camelia Gabriel
3.1 What Underlies Tissue Electromagnetic Properties?
34(7)
3.1.1 Ionic Conductivities
34(2)
3.1.2 Membranes and Solid Tissues
36(1)
3.1.3 Relaxation Models of Tissue Properties
37(4)
3.2 Overall Tissue Conductivities
41(5)
3.2.1 Properties of Fluids, Cell Suspensions and Blood
42(1)
3.2.1.1 Cerebrospinal Fluid
42(1)
3.2.1.2 Blood
42(1)
3.2.2 Bone
43(1)
3.2.3 Liver
44(1)
3.2.4 Lung
44(1)
3.2.5 Pathology
45(1)
3.2.5.1 Properties of Tumor Tissues
45(1)
3.2.5.2 Lung Injury
45(1)
3.2.5.3 Ischemia
45(1)
3.2.6 Active Membrane Properties
46(1)
3.3 Measurement of Impedance Properties
46(3)
3.3.1 Electrode Properties
47(1)
3.3.2 Conductivity Cell and Dependence on Geometry
48(1)
3.3.3 High Frequency (>50 MHz) Properties
48(1)
3.4 Tissue Anisotropy
49(2)
3.5 Electrical Safety and Current Limitations
51(1)
3.6 Conclusions and Perspective
52(1)
Chapter 4 Electronics and Hardware
53(26)
Gary J. Saulnier
4.1 Hardware Challenges and Approaches
53(4)
4.1.1 Speed and Precision
54(1)
4.1.2 Applied Currents vs. Voltages
55(1)
4.1.3 Pair-Drive vs. Parallel-Drive Systems
55(1)
4.1.4 Voltage Measurement on Current-Carrying Electrodes
56(1)
4.2 Electrode Excitation
57(16)
4.2.1 Current Sources
58(1)
4.2.1.1 Floating and Single-Ended Current Sources
59(1)
4.2.1.2 Current Source Requirements
60(1)
4.2.1.3 Stray Capacitance
61(1)
4.2.1.4 Current Source Compensation
63(1)
4.2.1.5 Current Source and Compensation Circuits
63(1)
4.2.1.6 Capacitance Mitigation Circuits
67(1)
4.2.2 Voltage Sources
68(1)
4.2.3 Connecting to Electrodes
69(1)
4.2.3.1 Cables
70(1)
4.2.3.2 Active Electrodes
71(1)
4.2.4 Multiplexers vs. Parallel Hardware
72(1)
4.3 Voltage Measurement
73(3)
4.3.1 Matched Filter
73(1)
4.3.1.1 Noise
74(1)
4.3.2 Differential vs. Single-Ended Voltage Measurement
74(2)
4.3.3 Common-mode Voltage Feedback
76(1)
4.4 EIT Systems
76(1)
4.5 Conclusion
77(2)
Chapter 5 The EIT Forward Problem
79(30)
Andy Adler
William R.B. Lionheart
5.1 Introduction
79(1)
5.2 Mathematical Setting
80(3)
5.2.1 Quasi-static Approximation
81(2)
5.3 Current Propagation in Conductive Bodies
83(5)
5.3.1 Analytical Solutions
85(1)
5.3.2 Circular Anomaly in a Unit Disk
86(1)
5.3.3 Detectability
87(1)
5.4 Measurements and Electrodes
88(3)
5.5 Measurement Strategy
91(4)
5.5.1 Linear Regression
92(1)
5.5.2 Adjacent Measurement Protocol
93(1)
5.5.3 Optimal Drive Patterns
93(2)
5.6 EIT Sensitivity
95(3)
5.6.1 Standard Formula for the Jacobian
96(2)
5.7 Solving the Forward Problem: The Finite Element Method
98(8)
5.7.1 Basic FEM Formulation
99(3)
5.7.2 Solving the Linear System
102(1)
5.7.3 Conjugate Gradient and Krylov Subspace Methods
103(1)
5.7.4 Mesh Generation
104(2)
5.8 Further Comments on the Forward Model
106(3)
Chapter 6 The EIT Inverse Problem
109(28)
William R.B. Lionheart
Andy Adler
6.1 Introduction
110(1)
6.2 Why is EIT So Hard?
111(1)
6.3 Inverse Problem
112(1)
6.4 Regularizing Linear Ill-posed Problems
113(6)
6.4.1 Ill-conditioning
113(1)
6.4.2 Tikhonov Regularization
114(1)
6.4.3 The Singular Value Decomposition
115(1)
6.4.4 Studying Ill-conditioning with the SVD
116(2)
6.4.5 More General Regularization
118(1)
6.5 Regularizing EIT
119(1)
6.6 Difference EIT
120(6)
6.6.1 Linearized EIT Reconstruction
121(1)
6.6.2 Selection of Hyperparameter, α
121(1)
6.6.3 Regularization Parameters
122(2)
6.6.4 Backprojection
124(1)
6.6.5 GREIT
124(2)
6.7 Absolute EIT
126(2)
6.7.1 Iterative Nonlinear Solution
127(1)
6.8 Total Variation Regularization
128(2)
6.9 Common Pitfalls and Best Practice
130(2)
6.10 Further Developments in Reconstruction Algorithms
132(2)
6.10.1 Beyond Tikhonov regularization
133(1)
6.10.2 Direct Non-linear Methods
133(1)
6.11 Machine Learning and Inverse Problems
134(1)
6.12 Practical Applications
135(2)
Chapter 7 D-bar Methods for EIT
137(14)
David Isaacson
Jennifer L. Mueller
Samuli Siltanen
7.1 Introduction
137(1)
7.2 Calderon's Method
138(3)
7.3 The Rise of the CGO Solutions
141(1)
7.4 A 2-D D-bar Method
141(6)
7.4.1 Equations of the D-bar Method
142(1)
7.4.2 Numerical Solution of the Equations
143(1)
7.4.2.1 Computing the DN Map from Measured Data
143(1)
7.4.2.2 Computation of the Scattering Transform
144(1)
7.4.2.3 Numerical Solution of the D-bar Equation
145(1)
7.4.3 Examples of Reconstructions
146(1)
7.5 New and Recent Directions in D-bar Methods
147(4)
Chapter 8 EIT Image Interpretation
151(26)
Zhanqi Zhao
Bin Yang
Lin Yang
8.1 Elements of EIT Images
152(4)
8.1.1 Valid Size of an Image
152(1)
8.1.2 Colour Mapping and Colour Scale
152(1)
8.1.3 Sampling Frequency and Mixture of Signals
153(2)
8.1.4 Meaning of the Pixels in Different Types of Images
155(1)
8.2 Functional Images and EIT Measures
156(13)
8.2.1 Simple Distribution of the Impedance Changes in Certain ROIs
157(1)
8.2.1.1 Example: Averaging Tidal Variation fEIT
157(1)
8.2.1.2 Example: Regression fEIT
157(1)
8.2.1.3 Example: Cardiac-related fEIT
158(1)
8.2.1.4 Example: Identifying and Tracking of Intracranial Resistivity Changes
159(1)
8.2.2 Differences of Impedance Variation Calculated in Spatial Correlations
160(1)
8.2.2.1 Example: Center of Ventilation (CoV)
161(1)
8.2.2.2 Example: The Global Inhomogeneity Index (GI)
161(1)
8.2.2.3 Example: Spatial Related Classification of Intracranial Resistivity Changes
162(1)
8.2.3 Subtracting Temporal Information (Taking Advantages of the High Sampling Rate)
163(1)
8.2.3.1 Example: Intra-tidal Volume Distribution (ITVD)
163(1)
8.2.3.2 Example: Regional Ventilation Delay RVD, both fEIT and Index Available
164(1)
8.2.4 New Units or Dimensions Deriving from Impedance
165(1)
8.2.4.1 Example: Regional Compliance as an fEIT and the Application in PEEP Titration
165(1)
8.2.4.2 Example: Regional Findings for Pulmonary Function Test
167(1)
8.2.4.3 Example: Determining Impedance Measures Using Contrast Agents
168(1)
8.2.4.4 Example: The Linear Correlation Metric Images for Frequency Difference EIT
168(1)
8.3 Clinical Applications
169(5)
8.3.1 Using Existing fEIT and Measures
169(1)
8.3.2 Recommendations for Development of fEIT Images and Measures
170(4)
8.4 List of fEIT Image and Measures
174(3)
Section III Applications
Chapter 9 EIT for Measurement of Lung Function
177(14)
Inez Frerichs
9.1 Introduction
177(2)
9.1.1 Basics of Lung Physiology and Pathophysiology
177(1)
9.1.2 Lung-related Applications of EIT
178(1)
9.2 EIT Examinations During Spontaneous Quiet Tidal Breathing
179(4)
9.2.1 Analysis of EIT Data Acquired During Spontaneous Quiet Tidal Breathing
180(2)
9.2.2 Findings of EIT Studies Performed in Human Subjects During Quiet Tidal Breathing
182(1)
9.3 EIT Examinations During Ventilation Manoeuvres and Pulmonary Function Testing
183(5)
9.3.1 Analysis of EIT Data Acquired During Ventilation Manoeuvres and Pulmonary Function Testing
184(3)
9.3.2 Findings of EIT Studies Performed in Human Subjects During Ventilation Manoeuvres and Pulmonary Function Testing
187(1)
9.4 Summary
188(3)
Chapter 10 EIT for Monitoring of Ventilation
191(16)
Tobias Becher
10.1 Introduction
191(1)
10.2 Assessment of Ventilation Distribution with EIT
192(2)
10.3 Measures of Ventilation Inhomogeneity
194(1)
10.4 Intratidal Ventilation Inhomogeneity and Alveolar Cycling
194(1)
10.5 Identification of Overdistension and Alveolar Collapse at the Bedside Using Regional Compliance Estimation
195(3)
10.6 Identification of Poorly Ventilated Lung Areas
198(1)
10.7 End-expiratory Lung Impedance Changes for Quantification of Lung Recruitment and Derecruitment
199(1)
10.8 Expiratory Time Constants for Monitoring Airflow Limitation
200(1)
10.9 Comparison of Different Approaches for Optimizing Mechanical Ventilation with EIT
201(4)
10.9.1 Gravity-dependent Ventilation Distribution
201(1)
10.9.2 Global Inhomogeneity Index and Coefficient of Variation
202(1)
10.9.3 Intratidal Ventilation Inhomogeneity
202(1)
10.9.4 Quantification of Alveolar Overdistension and Collapse During a Decremental PEEP Trial ("Costa-Approach")
202(1)
10.9.5 Assessment of Changes in Regional Compliance with Different VT or PEEP Level
203(1)
10.9.6 Poorly Ventilated Lung Areas ("Silent Spaces")
204(1)
10.9.7 Analyzing Changes in End-expiratory Lung Impedance
204(1)
10.9.8 Regional Expiratory Time Constants
205(1)
10.10 Conclusion
205(2)
Chapter 11 EIT Monitoring of Hemodynamics
207(24)
Lisa Krukewitt
Fabian Muller-Graf
Daniel A. Reuter
Stephan H. Bohm
Huaiwu He
11.1 Introduction
208(1)
11.2 Classical Methods and Key Parameters of Hemodynamic Measurements
209(4)
11.2.1 Intra-Vascular Pressure Measurement
209(1)
11.2.1.1 Systemic Arterial Pressure
209(1)
11.2.1.2 Pulmonary Arterial Pressure
209(1)
11.2.1.3 Systemic Venous Pressure
210(1)
11.2.1.4 Pulmonary Venous Pressure
210(1)
11.2.2 Flow Parameters
211(1)
11.2.3 Volume Status Parameters
211(1)
11.2.3.1 Stroke Volume Variation
211(1)
11.2.3.2 Extravascular Lung Water
212(1)
11.3 Origins of Cardiosynchronous Signals in EIT Measurements
213(1)
11.4 Interfering Signals in Hemodynamic EIT Measurements
214(5)
11.4.1 Blood Flow and Blood Volume Changes
215(1)
11.4.2 Ventilation
215(1)
11.4.2.1 Regions of Interest
216(1)
11.4.2.2 ECG-Gating
216(1)
11.4.2.3 Decomposition of Signals
217(1)
11.4.2.4 Apnea Measurements
218(1)
11.4.3 Heart Movement
218(1)
11.5 EIT Measurements for Hemodynamics
219(10)
11.5.1 Intra-Arterial Pressure Measurement by Pulse Transit Time
220(1)
11.5.1.1 Determining Pulse Arrival Times with EIT
220(1)
11.5.1.2 Aortic Blood Pressure
221(1)
11.5.1.3 Pulmonary Artery Pressure
222(1)
11.5.2 Flow Parameters
222(1)
11.5.2.1 Pulmonary Perfusion
222(1)
11.5.2.2 Regional V/Q Matching
223(1)
11.5.2.3 Cardiac Output and Stroke Volume
224(1)
11.5.3 Volume Status
225(1)
11.5.3.1 Stroke Volume Variation and Heart-Lung-Interaction
225(1)
11.5.3.2 Detection of Aortic ROI
227(1)
11.5.3.3 Extravascular Lung Water
228(1)
11.6 Summary and Outlook
229(2)
Chapter 12 EIT Imaging of Brain and Nerves
231(30)
David Holder
12.1 Introduction
232(2)
12.2 Physiological Basis of EIT of Brain Function
234(6)
12.2.1 Bioimpedance of Brain and Nerve and Changes During Activity or Pathological Conditions
234(1)
12.2.1.1 Impedance of Resting Brain
234(1)
12.2.1.2 Anoxic Depolarization and Cerebral Ischemia
235(1)
12.2.1.3 Slow Impedance Changes During Functional Activity
237(1)
12.2.1.4 Functional Activity with the Time Course of the Action Potential
238(1)
12.2.2 Other Mechanisms of Impedance Change: Temperature and CSF Movements
239(1)
12.2.3 Effect of Coverings of the Brain When Recording EIT with Scalp Electrodes
239(1)
12.3 EIT Systems Developed for Brain Imaging
240(9)
12.3.1 Hardware
240(1)
12.3.1.1 Relevant Instrumentation Principles
240(1)
12.3.1.2 EIT Systems Developed in the UCL Group
243(2)
12.3.2 Reconstruction Algorithms for EIT of Brain Function
245(1)
12.3.2.1 Reconstruction Algorithms for Time Difference EIT Imaging Based on a Linear Assumption
245(1)
12.3.2.2 Non-linear Reconstruction Algorithms for EIT of Brain Function
247(1)
12.3.3 Development of Tanks for Testing of EIT Systems
248(1)
12.4 EIT of Slow Impedance Changes in the Brain Related to Changes in Blood Volume and Cell Swelling
249(6)
12.4.1 During Physiological Evoked Activity
249(2)
12.4.2 EIT of Slow Changes During Epileptic Seizures
251(1)
12.4.2.1 Proof of Concept in Animal Studies
251(1)
12.4.2.2 Human Studies
254(1)
12.5 EIT in Cerebral Pathology Over Hours or Days
255(2)
12.5.1 Time Difference Cerebral EIT Over Hours or Days
255(1)
12.5.2 Multifrequency EIT in Acute Stroke
256(1)
12.6 EIT of Neuronal Depolarization
257(3)
12.6.1 Fast Neural in the Brain During Evoked Physiological Activity and Epileptic Seizures
258(1)
12.6.2 Fast Neural EIT in Peripheral Nerve
259(1)
12.7 Conclusion
260(1)
Chapter 13 EIT for Imaging of Cancer
261(28)
Ryan Halter
13.1 EIS in Cancer
263(2)
13.1.1 Cancer Biology and Impedance Contrast Mechanism
263(1)
13.1.2 Anatomy-specific Impedance Contrast
263(1)
13.1.2.1 Breast
264(1)
13.1.2.2 Prostate
265(1)
13.2 Breast EIT
265(15)
13.2.1 Introduction
265(1)
13.2.2 Other Methods in Use for Breast Cancer Detection
266(1)
13.2.3 Different Approaches to Breast EIT
267(1)
13.2.4 Impedance Mapping
267(1)
13.2.5 Tomographic Imaging
268(1)
13.2.6 Limitations of Impedance Measurements
268(1)
13.2.7 Advantages of Impedance as a Screening Tool
268(1)
13.2.8 Clinical Results Summaries
269(1)
13.2.9 Planar Geometry Systems
269(1)
13.2.9.1 Piperno 1990[ 830]
269(1)
13.2.9.2 Malich 2000[ 675]
269(1)
13.2.9.3 Cherepenin 2001[ 194]
270(1)
13.2.9.4 Malich 2001a[ 674]
270(1)
13.2.9.5 Malich 2001b[ 673]
270(1)
13.2.9.6 Cherepin 2002[ 196]
271(1)
13.2.9.7 Glickman 2002[ 351]
271(1)
13.2.9.8 Martin 2002[ 688]
272(1)
13.2.9.9 Stojadinovic 2005[ 1007]
272(1)
13.2.9.10 Stojadinovic 2006[ 1006]
272(1)
13.2.9.11 Trokhanova 2008[ 1055]
273(1)
13.2.9.12 Raneta 2013[ 862]
273(1)
13.2.10 Circular Geometry Systems
273(1)
13.2.10.1 Osterman 2000[ 796]
273(1)
13.2.10.2 Halter 2004, 2008c [ 405,406]
274(1)
13.2.10.3 Soni 2004[ 997]
275(1)
13.2.10.4 Poplack 2004[ 839]
276(1)
13.2.10.5 Poplack 2007[ 840]
276(1)
13.2.10.6 Halter 2008c[ 406]
277(1)
13.2.10.7 Halter 2015[ 408]
277(1)
13.2.10.8 Discussion of the Clinical Trials
278(2)
13.3 Prostate EIT
280(9)
13.3.1 Introduction
280(1)
13.3.2 Transrectal EIT for Cancer Detection and Biopsy Guidance
281(3)
13.3.3 Surgical Margin Assessment Using Endoscopic Electrode Arrays
284(2)
13.4 Other Cancer EIT
286(1)
13.5 Perspective on Cancer Imaging with EIT
286(3)
Chapter 14 Other Clinical Applications of EIT
289(6)
Ryan Halter
David Holder
14.1 Tumour Ablation Monitoring
289(2)
14.2 System-on-chip and Cell/tissue Imaging
291(1)
14.3 Wearables
292(1)
14.4 Intra-Pelvic Venous Congestion
293(1)
14.5 Other Potential Applications
293(2)
Chapter 15 Veterinary Applications of EIT
295(14)
Martina Mosing
Yves Moens
15.1 Introduction to Thoracic EIT in Veterinary Applications
295(1)
15.1.1 Creation of Finite Element Models for Animals
295(1)
15.2 Translational Research in Animals
296(2)
15.2.1 Pig
297(1)
15.2.2 Dog
297(1)
15.2.3 Lamb and Sheep
297(1)
15.2.4 Horse
298(1)
15.3 Clinical Research in Animals
298(6)
15.3.1 Horses
298(4)
15.3.2 Dog
302(1)
15.3.3 Rhinoceros
303(1)
15.4 Clinical Applications in Animals
304(1)
15.5 Future of EIT in Veterinary Applications
305(4)
Section IV Related Technologies
Chapter 16 Magnetic Induction Tomography
309(30)
Stuart Watson
Huw Griffiths
16.1 Introduction
309(1)
16.2 The MIT Signal
310(1)
16.3 MIT Array Design
311(5)
16.3.1 Excitors and Sensors
311(1)
16.3.2 Array Configuration
312(2)
16.3.3 Screening
314(1)
16.3.4 Cancellation of the Primary Signal
314(2)
16.4 Signal Demodulation
316(1)
16.5 Working Imaging Systems and Proposed Applications
317(8)
16.5.1 MIT for the Process Industry
317(1)
16.5.2 Security Applications
318(1)
16.5.3 Petrochemical Industry
319(1)
16.5.4 Biomedical MIT
320(5)
16.6 Image Reconstruction
325(2)
16.7 Spatial Resolution, Conductivity Resolution and Noise
327(2)
16.8 Propagation Delays
329(1)
16.9 Scaling the Size of an MIT Array
329(4)
16.10 Multifrequency Measurements: Magnetic Induction Spectroscopy
333(2)
16.11 Imaging Permittivity and Permeability
335(1)
16.12 Conclusions
336(3)
Chapter 17 Electrical Impedance Imaging Using MRI
339(44)
Oh In Kwon
Eung Je Woo
17.1 Introduction
340(2)
17.2 Conductivity, Permittivity, and Maxwell's Equations
342(1)
17.3 Magnetic Resonance Electrical Impedance Tomography (MREIT)
343(10)
17.3.1 Governing Equations in MREIT
343(1)
17.3.2 Measurement Techniques in MREIT
344(2)
17.3.3 Image Reconstruction Algorithms in MREIT
346(1)
17.3.3.1 Harmonic Bz Algorithm
346(1)
17.3.3.2 Projected Current Density Algorithm
348(1)
17.3.3.3 Direct Harmonic Bz Algorithm
349(1)
17.3.4 Clinical Applications of MREIT
350(1)
17.3.4.1 MREIT for Transcranial Direct Current Stimulation (tDCS)
351(1)
17.3.4.2 MREIT for Deep Brain Stimulation (DBS)
352(1)
17.3.5 Future Work in MREIT
352(1)
17.4 Magnetic Resonance Electrical Properties Tomography (MREPT)
353(7)
17.4.1 Governing Equation in MREPT
354(1)
17.4.2 Measurement Techniques in MREPT
355(1)
17.4.3 Image Reconstruction Algorithms in MREPT
356(1)
17.4.4 Clinical Applications of MREPT
357(1)
17.4.4.1 Imaging of Conductivity Changes Caused by Radiation Therapy (RT)
357(1)
17.4.4.2 Conductivity Imaging of Ischemic Stroke
357(1)
17.4.4.3 Conductivity Imaging of Lower Extremity
358(1)
17.4.5 Future Work in MREPT
359(1)
17.5 Frequency Dependence and Direction Dependence of Conductivity
360(4)
17.5.1 Frequency Dependence of Conductivity
360(1)
17.5.2 Direction Dependence of Conductivity
361(1)
17.5.3 Conductivity Tensor and Water Diffusion Tensor
362(2)
17.6 Diffusion Tensor Magnetic Resonance Electrical Impedance Tomography (DT-MREIT)
364(4)
17.6.1 Governing Equations in DT-MREIT
364(1)
17.6.2 Measurement Techniques in DT-MREIT
365(1)
17.6.3 Image Reconstruction Algorithms in DT-MREIT
366(1)
17.6.4 DT-MREIT Imaging Experiments
367(1)
17.7 Conductivity Tensor Imaging (CTI)
368(13)
17.7.1 Governing Equations of CTI
369(1)
17.7.2 Measurement Techniques
369(2)
17.7.3 Image Reconstruction Algorithms in CTI
371(1)
17.7.3.1 Extraction of dwe, dwi and α
371(1)
17.7.3.2 Estimation of Dwe
373(1)
17.7.3.3 Estimation of β
374(1)
17.7.4 CTI Imaging Experiments
375(1)
17.7.4.1 Conductivity Phantom with Giant Vesicle Suspension
375(1)
17.7.4.2 In Vivo Human Brain
375(2)
17.7.5 Clinical Applications of CTI
377(2)
17.7.6 Future Work in CTI
379(2)
17.8 Summary
381(2)
Chapter 18 Geophysical ERT
383(20)
Alistair Boyle
Paul Wilkinson
18.1 Introduction
383(1)
18.2 Common Applications
384(4)
18.3 Research Applications
388(4)
18.4 Complex Resistivity and Induced Polarization
392(1)
18.5 Logarithmic Parametrization
393(1)
18.6 Absolute Reconstruction
393(1)
18.7 Timelapse Inversion
394(1)
18.8 The Use of Electrode Models
395(1)
18.9 Modelling Open Domains
395(1)
18.10 2.5D Calculations
396(2)
18.11 Data Quality Measures
398(1)
18.12 Data Weighting
399(1)
18.13 Available Hardware
400(1)
18.14 Available Software
401(1)
18.15 Discussion
401(2)
Chapter 19 Industrial Process Tomography
403(20)
Thomas Rodgers
William Lionheart
Trevor York
19.1 Introduction
403(2)
19.2 Data Acquisition
405(3)
19.2.1 Electrical Resistance Tomography (ERT)
405(1)
19.2.2 Electrical Capacitance Tomography (ECT)
406(1)
19.2.3 Magnetic Induction Tomography (MIT)
407(1)
19.2.4 Electrical Impedance Tomography
407(1)
19.2.5 Intrinsically Safe Systems
408(1)
19.3 Previous Industrial Applications of Electrical Tomography
408(8)
19.3.1 Applications of Electrical Resistance Tomography Technology to Pharmaceutical Processes
408(2)
19.3.2 Imaging the Flow Profile of Molten Steel Through a Submerged Pouring Nozzle
410(1)
19.3.3 The Application of Electrical Resistance Tomography to a Large Volume Production Pressure Filter
411(2)
19.3.4 A Novel Tomographic Flow Analysis System
413(1)
19.3.5 Application of Electrical Capacitance Tomography for Measurement of Gas/Solids Flow Characteristics in a Pneumatic Conveying System
414(2)
19.4 Recent Industrial Applications of Electrical Tomography
416(5)
19.4.1 Application of Electiical Resistance Tomography to the Measurement of Batch Mixing
416(1)
19.4.2 Application of Electrical Resistance Tomography to Inline Flow Measurement
417(3)
19.4.3 Application of Electrical Resistance Tomography to Cleaning-in-Place
420(1)
19.5 Summary
421(2)
Chapter 20 Devices, History and Conferences
423(14)
20.1 EIT Conferences
423(2)
20.2 Historical Perspective
425(1)
20.3 EIT Hardware
426(11)
20.3.1 Commercial Systems
426(4)
20.3.1.1 Draeger Medical
426(2)
20.3.1.2 Sentec
428(2)
20.3.1.3 Sciospec
430(1)
20.3.2 Research Systems
430(7)
20.3.2.1 Sheffield Mk 2 System
430(1)
20.3.2.2 Goettingen GoeMFII System
431(1)
20.3.2.3 Ecole Polytechnique de Montreal
432(1)
20.3.2.4 Russian Academy of Sciences Breast Imaging System
432(1)
20.3.2.5 CRADL System
433(1)
20.3.2.6 Rensselaer Polytechnic Institute ACT 3 System
434(1)
20.3.2.7 KHU Mark2.5 System
434(1)
20.3.2.8 Dartmouth Broadband, High Frequency System
435(2)
Bibliography 437(58)
Index 495
David Holder is a Professor of Biophysics and Clinical Neurophysiology, and an Honorary Consultant in Clinical Neurophysiology at University College London and UCL Hospitals, UK.

Andy Adler is a Canada Research Professor in biomedical engineering in Systems and Computer Engineering at Carleton University in Ottawa, Canada.
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