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Instituto Tecnológico de Costa Rica
Electronics Engineering School
Master of Science in Electronics
Embedded Systems
Design of initial prototype for Electrical Impedance Tomography (EIT)
for the Human Forearm
Master's thesis presented in partial fulfillment of the requirements to obtain the degree of Master of Science in Electronics – Embedded Systems Major
Mariana Alvarenga López
Abstract
The purpose of this research is to evaluate the feasibility for implementing a portable, low-cost, non-invasive, safe for health and simple handling electrical impedance tomograph for the human forearm. This device will allow pinpointing the location of nerves in the forearm to apply electrical stimulation therapies in patients with neurological damage for helping them to regain arm mobility.
Since no commercial device or research prototype for the human forearm applications exist, it is required to evaluate the current state of art and its implications to determine the feasibility of designing an electrical impedance tomograph oriented to this application.
This thesis comprises the integration of the previous research stages, which correspond to the determination of the algorithms and the adequate reconstruction platform, as well as the development of the image reconstruction methodology to achieve the best approximation. This thesis is focused on the evaluation of the acquisition system and the definition of the functional and non-functional requirements, based on the preliminary results for its implementation.
The main achievement of this research was to determine the prototype specifications and to implement an initial version that allows to test the possibility of generating useful data to reconstruct an electrical impedance image using the EIDORs reconstruction software. The test data was obtained using a circular phantom with a diameter similar to an average forearm.
as an insulator and a 0.7 cm diameter stainless-steel cylinder. Both were placed individually in the center and in the periphery of the phantom to test its performance for these four reconstruction scenarios. One of the main advantages offered by the adjacent current injection pattern is a high sensitivity in the periphery; nevertheless, a greater approximation was obtained for the reconstruction of the objects in the center.
According to the obtained results, it is determined that the reconstruction of structures as fine as the nerves is not feasible using the current prototype; therefore
It is necessary to optimize the model (prototype) due to the different physical and electrical factors affecting its performance. These factors affecting the performance of the prototype are related to the corrosion of the metallic parts of the electrode array, as well as faulty contacts due to this corrosion or loose connections between the different stages.
Resumen
El objetivo de esta investigación es evaluar la factibilidad de la implementación de un tomógrafo por impedancia eléctrica para el antebrazo humano.Se busca que el dispositivo sea de bajo costo portátil, no invasivo y seguro para el paciente, el cual permitirá localizar la ubicación de los nervios en el antebrazo para aplicar terapias de estimulación eléctrica en pacientes con daño neurológico para ayudarles a recuperar la movilidad del brazo.
Debido a que no existen ni prototipos comerciales ni de experimentación para aplicaciones orientadas a esta área del cuerpo humano, es necesario evaluar el estado del arte y sus implicaciones para determinar la factibilidad de diseñar un tomógrafo de impedancia eléctrica orientado a esta aplicación.
Esta tesis constituye la integración de etapas previas de la investigación, las cuales corresponden a la determinación de los algoritmos y la plataforma de reconstrucción adecuada para la aplicación, así como el desarrollo de la metodología de reconstrucción de imagen para lograr la mejor aproximación. Esta tesis está enfocada en la evaluación del sistema de adquisición y la definición de los requerimientos funcionales y no funcionales, basados en los resultados preliminares, para la construcción del mismo.
El principal logro de esta investigación fue determinar las especificaciones del prototipo e implementar una versión inicial que permitió probar la posibilidad de generar una serie de datos útiles para reconstruir una imagen de impedancia eléctrica utilizando el software de reconstrucción EIDORs. Los datos de prueba fueron obtenidos utilizando un fantoma circular con un diámetro aproximado al de un antebrazo promedio.
como en la periferia del fantoma para probar su rendimiento para estos cuatro escenarios de reconstrucción. Una de las principales ventajas que ofrece el patrón de inyección de corriente adyacente es una alta sensibilidad en la periferia; sin embargo, se obtuvo una mayor aproximación para la reconstrucción de los objetos colocados en el centro.
De acuerdo con los resultados obtenidos se determina que no es factible la reconstrucción de estructuras tan finas como los nervios con el prototipo actual y por lo tanto es necesaria su optimización ya que hay diferentes factores físicos y eléctricos que afectan el rendimiento del mismo. Los principales factores que afectan el rendimiento del prototipo están relacionados con la corrosión de las piezas metálicas del arreglo de electrodos, así como falsos contactos debidos a esta corrosión o conexiones flojas entre las diferentes etapas.
Acknowledgment
I would like to thank to the following people and institutions that in one way or another have contributed to the development of this project.
My advisor MSc. Marta Eugenia Vílchez Monge and the members of the Examination Committee Dr. Renato Rímolo Donadio and MBA Esteban Baradín, professors of Instituto Tecnológico de Costa Rica, for its guidance and all their support during this thesis research.
Víctor Bermúdez, Michael Martínez and the Canam Technology, Inc. team for supporting me as Master`s degree student and for their contribution with hardware and technical resources for this prototype implementation.
National Instruments for the willingness to contribute with software, hardware and technical resources for a possible implementation.
CONICIT "Consejo Nacional para Investigaciones Científicas y Tecnológicas" and MICIT "Ministerio de Ciencia, Tecnología y Telecomunicaciones" for providing financial support to complete my studies.
I also want to extend my gratitude to my mother for her support in every step I have taken in my life.
And finally, I am thankful with my beloved David, Valeria and Daniela, thank you for your understanding and all your patience, thank you for being my biggest motivation and for making possible my goals achievement, thank you for so much love.
To my family...
Table of Contents
Abstract ... 4
Resumen ... 6
Acknowledgment ... 8
Table of Contents ... 10
List of Figures ... 12
List of Tables ... 14
List of Acronyms ... 15
Chapter 1 Introduction ... 17
1.1 MOTIVATION AND PREVIOUS WORK ... 17
1.2 GOAL ... 19
1.3 OBJECTIVES ... 19
1.3.1 General Objective... 19
1.3.2 Specific Objectives ... 19
Chapter 2 General fundamentals ... 20
2.1 ELECTRICAL IMPEDANCE TOMOGRAPHY (EIT)OVERVIEW ... 20
2.2 ELECTRODE ARRAY ARRANGEMENT ... 22
2.3 EIT SYSTEM ELECTRONIC INSTRUMENTATION ... 23
2.3.1 Constant current injector ... 24
2.3.2 Data acquisition system ... 25
2.3.3 Signal conditioner ... 25
2.3.4 Switching and control module ... 26
2.4 CURRENT DRIVE AND VOLTAGE ACQUISITION PATTERNS ... 26
2.4.1 Adjacent or neighboring current drive pattern ... 27
2.4.2 Opposite or polar current drive pattern ... 28
2.4.3 Diagonal or cross current drive pattern ... 30
2.4.4 Trigonometric or adaptive current drive pattern ... 31
2.4.5 Current drive patterns characteristics summary chart ... 32
2.5 CONDUCTIVITY ... 33
2.6 BIOLOGICAL TISSUES ELECTRICAL BEHAVIOR REVIEW... 35
2.7 THE HUMAN FOREARM... 36
2.8 IMAGE RECONSTRUCTION ... 38
2.8.1 Mathematical principles ... 40
2.8.2 Forward Problem ... 43
2.8.3 Inverse Problem ... 44
2.9 CLINICAL APPLICATIONS ... 45
Chapter 3 Evaluation of the scope and limitations of previous implementations developed for EIT systems ... 47
3.1 EIT SYSTEM COMPONENT QUALITATIVE EVALUATION ... 47
3.2 REVIEW OF ELECTRONIC INSTRUMENTATION EMPLOYED FOR PREVIOUS EIT SYSTEM ASSEMBLIES ... 55
Chapter 4 Prototype definition ... 60
4.1 PROTOTYPE REQUIREMENTS ... 60
4.1.1 Functional Requirements ... 60
4.1.2 Non-functional Requirements ... 63
4.2 COMPLIANCE OPTIONS ANALYSIS ... 63
4.2.1 Design based on National Instruments myDAQ ... 63
4.2.3 Design based on Arduino platform ... 67
4.2.4 Proposed designs main components and requirements compliance ... 68
4.3 PROTOTYPE SPECIFICATIONS ... 70
4.4 PROTOTYPE DESIGN ... 71
4.4.1 Arduino platform ... 72
4.4.2 IO expander Mux Shield ... 72
4.4.3 Data Acquisition Shield ARD-LTC2499 ... 75
4.4.4 Constant current injector module ... 77
4.4.5 Cabling ... 80
4.4.6 Electrodes array ... 83
4.4.7 Test phantom characterization ... 84
4.5 ELECTRICAL VALIDATION ... 85
Chapter 5 Prototype validation ... 91
5.1 RECONSTRUCTION TARGET SIMULATIONS ... 92
5.2 DATA ACQUISITION PROCESS ... 97
5.3 IMAGE RECONSTRUCTION PROCESS OF PHANTOM TEST RESULTS ... 100
Chapter 6 Results and Analysis ... 101
6.1 POTENTIALS BOUNDARIES BEHAVIOR ... 101
6.2 SIMULATED VERSUS EXPERIMENTAL DIFFERENTIAL VOLTAGES ... 106
6.3 INHOMOGENEOUS DIFFERENTIAL VOLTAGES RESPONSES AND RECONSTRUCTED IMAGES FOR TEST TARGETS. ... 109
6.3.1 PLASTIC CONTAINER INSERTED IN THE CENTER OF THE PHANTOM ... 110
6.3.2 PLASTIC CONTAINER INSERTED BETWEEN ELECTRODES E16 AND E1 ... 111
6.3.3 STAINLESS-STEEL CYLINDER INSERTED IN THE CENTER OF THE PHANTOM ... 113
6.3.4 STAINLESS-STEEL CYLINDER INSERTED BETWEEN ELECTRODES E16 AND E1 ... 114
6.3.5 GENERAL OBSERVATIONS ABOUT TARGET DIFFERENTIAL VOLTAGES ... 116
6.4 ISSUES FIND FOR PROTOTYPE ... 116
Chapter 7 Conclusions and Recommendations ... 119
7.1 CONCLUSIONS ... 119
7.2 RECOMMENDATIONS ... 121
Bibliography ... 122
Annex A Arduino shields schematics ... 126
Appendix A Phantom electrode plans ... 128
Appendix B Arduino source code ... 129
Appendix C EIDORs simulation source code ... 133
Appendix D Vi measurement results ... 136
List of Figures
Figure 2-1. EIT system main components. ... 21
Figure 2-2. Sequence of voltage measurements for first current injection when using adjacent current drive pattern for 16 equidistantly spaced electrodes array. ... 28
Figure 2-3. Sequence of voltage measurements for first current injection when using opposite current drive pattern for 16 equidistantly spaced electrodes array. ... 29
Figure 2-4. Sequence of voltage measurements for first current injection when using cross current drive pattern for 16 equidistantly spaced electrodes array. ... 30
Figure 2-5. Sequence of voltage measurements for first current injection when using trigonometric current drive pattern for 16 equidistantly spaced electrodes array. ... 31
Figure 2-6. Equivalent circuit of bioelectrical impedance. ... 35
Figure 2-7. Depiction of cross-section showing all the structures in forearm (Image reproduced from [2]). ... 36
Figure 2-8. EIT image reconstruction process general diagram. ... 39
Figure 2-9. EIT image reconstruction process using an iterative algorithm diagram (Adapted from [30]). 40 Figure 2-10. Forward problem diagram. ... 43
Figure 2-11. Inverse problem diagram. ... 44
Figure 3-1. EIT system electronic instrumentation general block diagram. ... 56
Figure 4-1. Design using myDAQ for data acquisition and control. ... 64
Figure 4-2. NI myDAQ ADC hardware related block diagram (reproduced from [47]). ... 65
Figure 4-3. Design using PIC32 for data acquisition and control. ... 66
Figure 4-4. Block diagram using Arduino platform for control and external data acquisition card.... 67
Figure 4-5. Block diagram of final prototype based on Arduino platform. ... 71
Figure 4-6. Mux Shield (IO expander) for Arduino. ... 73
Figure 4-7. Switching module using Mux Shield board block diagram. ... 74
Figure 4-8. 16-Channel 24-Bit ADC data acquisition Arduino shield. ... 75
Figure 4-9. Optional external amplifiers connected to multiplexer outputs (Image reproduced from [51]) . 76 Figure 4-10. Basic Howland Current Pump (Image adapted from [52])... 77
Figure 4-11. Prototype Howland Current Pump schematic diagram. ... 79
Figure 4-12. Prototype Howland Current Pump mounted in breadboard. ... 79
Figure 4-13. Main conductor and shield being drive by the same input (Image reproduced from [13]) ... 80
Figure 4-14. Coaxial cables used for prototype. ... 81
Figure 4-15. Coaxial cables connected to electronic instrumentation side. ... 81
Figure 4-16. Coaxial cables connected to electrodes side. ... 81
Figure 4-17. Coupling resistances for sharing coaxial cables between MUX1 and MUX 2 outputs and ADC inputs. ... 82
Figure 4-18. Coupling circuit for each electrode. ... 82
Figure 4-19. Rear and front views of electrodes. ... 83
Figure 4-20. (a)Top view of phantom without inhomogeneities. (b) Top view of phantom with 3 cm diameter plastic container as inhomogeneity. (c) Top view of phantom with 0.7 cm diameter stainless-steel cylinder as inhomogeneity. ... 84
Figure 4-21. Current injection adjacent electrodes testing circuit. ... 86
Figure 4-22. Current injection adjacent electrodes equivalent testing circuit. ... 86
Figure 4-23. Distance between two adjacent electrodes. ... 87
Figure 5-2. Phantom diagram for determining inhomogeneities central axis location. (a) 3 cm diameter plastic container in the center. (b) 3 cm diameter plastic container between electrodes E16 and E1. (c) 0.7 cm diameter stainless-steel cylinder in the center. (d) 0.7 cm diameter stainless-steel cylinder between
electrodes E16 and E1. ... 93
Figure 5-3. Generated FEM for the test cases using unitary conductivity for the medium and "1.1" conductivity for the inserted cylinder. (a) 3 cm diameter plastic container in the center. (b) 3 cm diameter plastic container between electrodes E16 and E1. (c) 0.7 cm diameter stainless-steel cylinder in the center. (d) 0.7 cm diameter stainless-steel cylinder between electrodes E16 and E1. ... 94
Figure 5-4. Reconstructed images with the conductivities distribution for simulated test cases (a) 3 cm diameter plastic container in the center. (b) 3 cm diameter plastic container between electrodes E16 and E1. (c) 0.7 cm diameter stainless-steel cylinder in the center. (d) 0.7 cm diameter stainless-steel cylinder between electrodes E16 and E1. ... 96
Figure 5-5. Data acquisition process flow diagram ... 97
Figure 5-6. Data acquisition process output ... 99
Figure 5-7. Reconstructed images using the collected voltage measurements (a) 3 cm diameter plastic container in the center. (b) 3 cm diameter plastic container between electrodes E16 and E1. (c) 0.7 cm diameter stainless-steel cylinder in the center. (d) 0.7 cm diameter stainless-steel cylinder between electrodes E16 and E1. ... 100
Figure 6-1. Phantom current injection adjacent electrodes pair equivalent circuit. ... 102
Figure 6-2. Homogeneous voltage tendency for different current injection cycles. (a) E1-E2. (b) E2-E3. (c) E5-E6. (d) E9-E10. (e) E15-E16. (f) E16-E1. ... 105
Figure 6-3. Simulated homogeneous differential voltages set. ... 107
Figure 6-4. Experimental homogeneous differential voltages set. ... 107
Figure 6-5. Differential voltages tendency for target using the 3 cm diameter plastic container inserted in the center. (a), (b) and (c) are the homogeneous (Vh), inhomogeneous (Vi) and their difference (Vi -Vh), for simulated results; (d), (e) and (f) , are the same voltages for the experimental results. ... 110
Figure 6-7. Differential voltages tendency for target using the 3 cm diameter plastic container inserted between electrodes E16 and E1. (a), (b) and (c) are the homogeneous (Vh), inhomogeneous (Vi) and their difference (Vi -Vh), for simulated results; (d), (e) and (f) , are the same voltages for the experimental results. ... 112
Figure 6-8. Reconstructed images for target using the 3-cm diameter plastic container inserted between electrodes E16 and E1. (a) Simulated. (b) Experimental.... 112
Figure 6-9. Differential voltages tendency for target using the 0.7 cm diameter stainless-steel cylinder inserted in the center. (a), (b) and (c) are the homogeneous (Vh), inhomogeneous (Vi) and their difference (Vi -Vh), for simulated results; (d), (e) and (f) , are the same voltages for the experimental results. ... 113
Figure 6-10. Reconstructed images for target using the 0.7 cm diameter stainless-steel cylinder inserted in the center. (a) Simulated. (b) Experimental. ... 114
Figure 6-11. Differential voltages tendency for target using 0.7 cm diameter stainless-steel cylinder inserted between electrodes E16 and E1. (a), (b) and (c) are the homogeneous (Vh), inhomogeneous (Vi) and their difference (Vi -Vh), for simulated results; (d), (e) and (f) , are the same voltages for the experimental results. ... 115
Figure 6-12. Reconstructed images for target using the 0.7 cm diameter stainless-steel cylinder inserted between electrodes E16 and E1. (a) Simulated. (b) Experimental. ... 115
Figure A-1. Mux Shield Schematic. ... 126
Figure A-2. ARD-LTC2499 Schematic. ... 127
List of Tables
Table 2-1. Current drive patterns comparison chart ... 32
Table 2-2. Theoretical values of conductivity and relative permittivity for different tissues in the human forearm (from [26] and [27]). ... 37
Table 3-1. EIT system main components qualitative evaluation criteria ... 47
Table 3-2. List of articles and thesis documents collection reviewed for EIT system components qualitative evaluation ... 49
Table 3-3. Research results for EIT system main components qualitative evaluation... 50
Table 3-4. List of articles and thesis documents collection reviewed for EIT electronic instrumentation .... 55
Table 3-5. Electronic instrumentation for EIT assembled systems described in literature. ... 57
Table 4-1. Cost evaluation for main components required for the design based on NI myDAQ. ... 65
Table 4-2. Cost evaluation of the main components required for implementation based on PIC32 ... 67
Table 4-3. Cost evaluation for main components required for implementation based on Arduino ... 68
Table 4-4. Electronic instrumentation main components for different designs... 69
Table 4-5. Compliance requirements for different designs ... 69
Table 4-6. Neighbor electrodes pair enabled for current injection for each selection pins combination. .... 74
Table 4-7. Theoretical values of resistivity and conductivity for materials used for phantom (values from [22]). ... 85
Table 4-8. Voltage measured for each of the injection electrodes pair @ 1.065 ± 0.001 mA constant current ... 89
Table 4-9. Voltage differences and error rate for V_E1 and V_E2 ... 89
Table 5-1. Quantity of elements for each of the test cases resulting FEM ... 93
Table 6-2. Voltage differences and error rate for V_E1 and V_E2 ... 103
Table 6-4. Current injection and voltage measurement electrodes pair relation for greatest differential voltages in simulated data set. ... 108
Table 6-5. Current injection and voltage measurement electrodes pair relation for greatest differential voltages in experimental data set. ... 108
Table 6-6. Homogeneous potentials boundaries per electrode ... 117
Table D-1. Inhomogeneous potentials boundaries per electrode when using 3 cm diameter plastic container in the center ... 136
Table D-2. Inhomogeneous potentials boundaries per electrode when using 3 cm diameter plastic container between electrodes E16 and E1 ... 137
Table D-3. Inhomogeneous potentials boundaries per electrode when using 0.7 cm diameter stainless-steel cylinder in the center. ... 138
Table D-4. Inhomogeneous potentials boundaries per electrode when using 0.7 cm diameter stainless-steel cylinder between electrodes E16 and E1. ... 138
List of Acronyms
Acronyms Definition
2D or 3D: Two or three dimensional ADC: Analog to Digital Converter ARC: Argonaut RISC Core
C++: Object-oriented general purpose programming language CCI Constant Current Injector
CM: Continuous model
DAC: Digital to Analog Converter DAQ: Data Acquisition
DDS: Direct Digital Synthesis DIO: Digital Input/Output
DSP: Digital Signal Processor/Processing EC: Electric Conductivity
EIDORS: Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software
FEM: Finite element Method
FPGA: Field Programmable Gate Array I2C: Inter-Integrated Circuit
IC: Integrated Circuit
LGPL: Lesser General Public License
LVTTL: Low-Voltage Transistor-Transistor Logic (3.3 V)
MATLAB: Matrix Laboratory. It is a multi-paradigm numerical computing environment and fourth-generation programming language
NETGEN: It is an open source, under the conditions of the LGPL, automatic mesh generation tool for two and three dimensions linked to EIDORS.
Msps: Mega- samples per second NI: National Instruments
PIC: Registered trademarks of Microchip Technology for a family of microcontrollers
RC: Resistor-Capacitor SNR: Signal to noise ratio SPI: Serial Peripheral Interface SUT: Subject Under Test TI: Texas Instruments
Chapter 1
Introduction
1.1 Motivation and previous work
Currently, the Technische Universität Hamburg-Harburg (TUHH) is working out in human tissues equivalent circuits for studying its behavior under neuromuscular electrical stimulation experiments with the purpose of developing therapies for helping neurologically impaired patients to recover arm mobility.
Although there are no documented statistics about the number of patients with neurological damage suffering upper extremity disabilities, this condition can affect people of all ages because of its multiple causes, from craniocerebral trauma to degenerative diseases which require extensive, expensive and even uncomfortable and painful therapies; therefore, the positive results of using these electrical stimulation therapy will benefit a large percentage of this population.
To apply the proper electrical stimulation therapy, it is required to identify the nerves location in the forearm using a medical device that can be employed repeatedly and extended into the patient without exposure to any risk. Since, there is not a commercial device or research prototype for a portable, a low-cost and non-invasive device that allows pinpointing the location of nerves, the Instituto Tecnológico de Costa Rica in collaboration with the Technische Universität Hamburg-Harburg is working on the project denominated "Development of an Electrical Impedance Tomograph for Human Forearm" in order to assess the feasibility of eventual development of this device, which is based in the results of previous work "Finite Element Method Simulation Study of Electrical Impedance Tomography (EIT) for the Human Forearm" [1].
reconstruction procedure and the other oriented to the evaluation of electronic instrumentation requirements for data acquisition hardware.
The first stage corresponds to the thesis research "Electrical Impedance Tomography (EIT) Image reconstruction for the Human Forearm" [2] where the accurate methodology and its corresponding benchmark is defined in order to evaluate the required parameters to setup the EIT image reconstruction algorithm for obtaining a high precision human forearm imaging that allows pinpointing the location of nerves.
This research is oriented to the implementation of an acquisition system for the Electrical Impedance Tomograph useful to generate a data set that allows reconstructing an electrical impedance image using the EIDORs reconstruction software.
1.2 Goal
Prove the possibility for generating a database that allows the Electrical Impedance Tomography image reconstruction using EIDORS software.
1.3 Objectives
1.3.1 General Objective
Implement an acquisition and electrical signal conditioning system for an electrical impedance tomograph for the human forearm.
1.3.2 Specific Objectives
Assess the scope and limitations of other solutions implemented for EIT.
Design the data acquisition system.
Design an interface for signal conditioning according to the reconstruction software requirements.
Chapter 2
General fundamentals
This chapter introduces the general fundamentals of electrical impedance tomography required for the development of this thesis. It also includes, a brief description of electrodes array arrangement and the typical electronic instrumentation as well as an overview of the four well-known patterns for current drive and voltage measurements. Moreover, a review of conductivity principles, biological tissues electrical behavior, the human forearm, test phantom characterization and concepts as inverse and forward problems are presented in this chapter. Finally, it contains a brief history about clinical applications.
2.1 Electrical Impedance Tomography (EIT) Overview
The electrical impedance tomography (EIT) is an imaging method used to obtain low resolution images for medical and industrial applications based on the determination of the impedance distribution of an conductive medium, given simultaneous measurements of direct or alternating electric currents and voltages at the boundary of the medium [3].
Regarding clinical applications, EIT take advantage, as explained in [4], of the fact that biological tissues are compound of cells, intracellular and extracellular fluids whose behavior under the influence of external electric fields is determinate by complex bioelectrical impedance with high resistivity and low reactance components. Then, the bioelectrical impedance is a function of tissue composition and frequency of the applied AC signal [4]. EIT has the potential to be of clinical value and it plays an important role in diagnosing and monitoring a number of disease conditions, from the detection of breast cancer to monitoring brain function and possibly stroke [5]. Currently, it has been widely applied in the medical domain for the assessment of cardiac function, pulmonary hypertension, and lung function [3].
modeling parameters such as the electrodes and phantom geometry experimental errors in the boundary data [6]. EIT is a non-linear ill-posed inverse problem that is highly affected by errors in measurements due large changes in impedance imply only small changes in surface potentials and therefore the reconstruction process depends continuously on the data [7]. The no linearity problems occur because the low-frequency electrical current cannot be confined to a plane. Any change in conductivity in any place of the domain can affect all between potential boundary results [8] and between different measurements.
In order to convert ill-posed problem in a well-posed problem, it is required to apply regularization techniques [6]. Although, regularization techniques contribute to obtain an approach to true impedance distribution within the spatial resolution that is degraded.
According to [9] a modern EIT is compound by the following four main parts:
EIT sensors or electrode array.
Electronic instrumentation.
Phantom or Subject Under Test (SUT).
Computing system running reconstruction algorithm.
Figure 2-1. EIT system main components.
The first and second one corresponds to the data acquisition stage; and the last one, to the image reconstruction processing. This research is focused on data acquisition stage, the image reconstruction processing was evaluated in [2].
The EIT main components are described in the following sections.
2.2 Electrode array arrangement
Although according to literature, EIT systems use 8, 16, up to 64 electrodes distributed in one or more arrays around the boundary of subject under test, the EIT devices usually employ an 16 or 32 electrodes array equally spaced around the model periphery [10], [6].
Some EIT devices use more than one electrodes array to recover the current spread out of the assumed 2D conductivity distribution plane or for 3D image reconstruction. The electrical currents are not confined in the plane of electrode array, it rather spreads over a 3D space over the medium [4] affecting the 2D reconstruction.
The more electrodes, the greater the number of voltage measurements and lower voltage levels contributing directly to a higher spatial resolution. Nevertheless, EIT can be extremely affected by uncertainty errors like electrode placement and contour changes affecting the electrodes-boundaries' contact interfaces. Having more electrodes increase these uncertainties sources. Moreover, because the adjacent electrodes conductivity is inversely proportional to electrodes separation, when using adjacent current drive pattern, the measured voltages are lower and this introduces another source of uncertainty.
The electrodes array size must be selected for offering the higher spatial resolution and the lower uncertainties. As explained in [1], the separation between electrodes have to be greater than the electrodes width. Seeking to accommodate a 16 electrodes array in a median forearm circumference of 28 cm, the electrodes width can be determined as follows:
Now, in order to keep the patient safe and comfortable, the electrodes array must be considered. According to the results observed by Kuhn [11] on his study to demonstrate the influence of different sizes of electrodes used for the current injection on pain thresholds for different fat layer thicknesses and nerve depths, the optimum electrodes size to guarantee good comfort and selectivity levels for thin fat layers (0.25 cm) and superficial nerves (0.1 cm) in human forearm is 0.8 cm x 0.8 cm. Therefore, for this thesis purposes electrodes of 0.8 cm x 0.8 cm with a 0.95 cm separation will be used.
2.3 EIT system electronic instrumentation
The EIT electronic instrumentation main components consist in a constant current injector, a signal conditioner, a data acquisition system, a switching module and a controller. The constant current injector is used to inject low magnitude and low frequency current to the phantom or the SUT. The data acquisition system is employed for voltages measurements in the boundary. Those current injection and voltage measurements must be switched between different electrodes pairs; this process is performed by a switching module. The switching and synchronization of these modules are performed by a controller stage which is also in charge to communicate the retrieved information to a computer that runs the reconstruction algorithm.
These reconstruction algorithms will reconstruct images of internal impedance of biological tissue using voltages measurements from boundary obtained with the data acquisition system. Recently, most of the reconstruction algorithms have been implemented in the Electrical Impedance and Diffuse Optical Reconstruction Software project (EIDORS) set up by Lionheart and Adler [5].
impedance, contact area and boundary shape under the electrode as well as the electrode movement must be previously studied using simulated and phantom data [12]. The current is supplied to the electrodes and the voltages measured through shielded coaxial cables. These cables are usually of one meter or two long. In these cables a driven shield is used to protect the signals from noise, as well as to minimize the cable capacitance and capacitance variation when the cables are flexed [13].
Each one of the EIT electronic instrumentation main components are described in the following sections.
2.3.1 Constant current injector
Typically, EIT systems employ current stimulation; but voltage stimulation is also possible. The voltage stimulation systems are useful for phantoms where the electrode contact impedance and the internal conductivity are known variables. However, due to the SUT bioimpedance and the contact impedance between the electrode and the SUT are unknown variables, when using voltage injection, the current being injected through can overcome the maximum allowed before to cause injuries to patient. In this way, current stimulation is better than voltage, because it accounts for electrode contact impedance [14] and therefore, it contributes to minimize the sensitivity of the changes of the contact impedance between the electrode and the medium to measure [15].
The EIT systems can be classified according to the number of current sources, either as a single source system or a multiple source system [13]. The quantity of current sources is determined also by the current drive pattern being used. The adjacent or opposite current drive patterns use a single current source, meanwhile the adaptive pattern method requires a multiple source system.
As demonstrated by Kuhn in his study, the maximum current to be injected in a patient for keeping the pain below the thresholds is limited by the electrodes area. By using 0.8 cm x 0.8 cm electrodes, the maximum current to be injected through is:
(2.2)
2.3.2 Data acquisition system
The data acquisition corresponds to the stage employed for measuring the electrode voltages and then converting them to digital signal that is transmitted to the computer system that run the reconstruction algorithm. The data acquisition can be done using either single-ended or differential voltage measurements. Differential voltage measurement between pairs of electrodes is often used to reduce the dynamic range of requirements relative to single-ended (referenced to ground) voltage measurements [13].
The data acquisition module can be implemented by employing either manufactured data acquisition cards, ADC pins available in a microcontroller or ADC ICs managed by a microcontroller. The criteria for selecting the proper ADC can be reduced to main facts as resolution, accuracy, conversion speed and operation voltage range and other secondary features as multiplexing, internal or external reference and digital interface.
2.3.3 Signal conditioner
2.3.4 Switching and control module
A switching stage is required when the system uses a single source for current injector or have less ADC than electrodes pair for switching the current injection and voltage measurements between the different electrodes pairs. The switching stage is commonly build using analog multiplexers arrays.
Analog multiplexers, as explained in [13], present some non-ideal properties as nonzero 'on' resistance, limited 'off' isolation, the charge injection during switching and the relatively large input and output capacitances that introduce some uncertainties for EIT systems performance. The most critical problem is the input and output capacitances. To avoid these uncertain effects, it would be required to disregard the use of multiplexers as much ADCs and currents sources as electrodes that are in the system.
The control stage executes the switching and synchronization of data acquisition and current drive analog multiplexers arrays. It also stores the read values and eases the interface for the computer running the reconstruction algorithm. This controller stage, it can be implemented using a microcontroller like the ones embedded in Arduino platforms or the PIC family.
2.4 Current drive and voltage acquisition patterns
EIT systems allow to reconstruct images of internal impedance of biological tissue by using voltages’ measurements taken from boundary through an array of electrodes disposed around it.
For the two-electrode method, the voltage is measured through the same pair of electrodes used for current injection causing a voltage loss dependent on its contact impedance magnitude. For the four-electrode method, also known as tetrapolar, the current is injected through a pair of electrodes while a voltage measurement is taken using a different pair of electrodes at time, offering the great advantage that only the transfer impedance of the tissue between the potential reading electrodes is determined [16].
Four well-known patterns for current drive and voltage measurements [18], [7] are briefly described in this document as follows.
2.4.1 Adjacent or neighboring current drive pattern
According to literature, traditionally this driven method, suggested by Brown and Segar in 1987, is used for a large amount EIT applications for discovering the electrical impedance of the of object. As shown in Figure 2-2, this method proposes to inject a known current “I” to a first pair of electrodes and measure the differential voltages “Vn” at the remaining electrodes pairs by discarding the electrodes pair used for current injection [7]. The current injection/voltage measurements cycle is repeated until the current has been injected through all electrodes in the array.
The current projection through each pair of electrodes generates N-3 differential voltage data for an N-electrode array size. All these voltage measurements per current injection cycle are independent. Then, by using the electrodes array size of 16, due 13 independent voltage measurements are taken per each of 16 current injection cycles, it is possible to gather up to 208 different voltage measurements (16x13).
measurements are also affected by perturbations in the boundary of the object and they are quite sensitive to measurement error and noise [14].
Figure 2-2. Sequence of voltage measurements for first current injection when using adjacent current drive pattern for 16 equidistantly spaced electrodes array.
2.4.2 Opposite or polar current drive pattern
The opposite or polar current drive pattern was presented by Hua, Webster, and Tompkins in 1987 [18]. In this case, similar to adjacent method, a known current “I” is applied through a pair of electrodes that are 180◦ apart while the differential voltages “Vn” are measured on the remaining electrodes, using as voltage reference electrode, the one adjacent to the current-injecting electrode [7].
The current injection / voltage measurements cycle is repeated until the current has been injected through all pairs of electrodes in the array. The first and second
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current injection / voltage measurements cycle for a 16 equidistantly spaced electrodes array are depicted in Figure 2-3.
Figure 2-3. Sequence of voltage measurements for first current injection when using opposite current drive pattern for 16 equidistantly spaced electrodes array.
The current projection through each opposite current injection pair of electrodes generates N-3 differential voltage data for an N-electrode array size. In a system with 16 electrodes array there are 13 remaining electrodes for each of the 8 opposite current injection pairs that implies 104 (8x13) different voltage measurements.
Unlike to the adjacent pattern for a system with the same number of electrodes, the number of current injections is reduced in a half but it also decreases the achievable image resolution [19]. On the other hand, this method deploys a more uniform current density at the center of the object offering a better distribution of the sensitivity. Since the current travels with greater uniformity through the imaged body, it is less sensitive to conductivity changes at the boundary [7].
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2.4.3 Diagonal or cross current drive pattern
This drive pattern was also proposed by Hua, Webster, and Tompkins in 1987 [18]. This method is rarely used due to its sensitivity in the boundary because it is no good as the adjacent method; however, it has better sensitivity over the complete region.
In this pattern, adjacent electrodes are selected as current and voltage references. Then, a known current is injected between all the N-2 remaining electrodes. For an N-electrode array size, differential voltage measurements are taken by always using the same electrode as reference against the other N-3 electrodes. Figure 2-4 shows the representation of the first current injection/voltage measurements cycle for a 16 equidistantly spaced electrodes array.
Figure 2-4. Sequence of voltage measurements for first current injection when using cross current drive pattern for 16 equidistantly spaced electrodes array.
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For this method, N-3 differential voltage measurements are taken per each of the N-2 current injection cycles; therefore, when using a 16 electrodes array a collection of 182 (14x13) surface voltage measurements are obtained [18].
2.4.4 Trigonometric or adaptive current drive pattern
The adaptive pattern method is the unique four-electrode method from the four described in this document. It was proposed by Gisser, Isaacson, and Newell in 1987 [18]. Unlike the previous described current drive patterns, in this method the current is injected in all electrodes at the same time, requiring as many current injectors with unknown contact impedance as the electrodes quantity. A unique electrode is used as reference for boundary potential measurement; then, there is N-1 different measurements for an N-electrode array size. The current projection is then rotated by one electrode increment and other projections are obtained.
Figure 2-5. Sequence of voltage measurements for first current injection when using trigonometric current drive pattern for 16 equidistantly spaced electrodes array.
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For a 16 electrode array size this pattern produces eight different current projections yielding 120 (8x15) independent voltage data [7]. The first current projection of trigonometric current driven pattern is depicted in Figure 2-5.
Regarding the sensitivity to conductivity, it increases the unknown contact impedance of each current driver affecting the results used for the reconstruction.
2.4.5 Current drive patterns characteristics summary chart
In order to ease the comparison of the current drive patterns described above, the table below summarizes the main characteristics of them, as well as the quantity of current injection cycles, voltage measures per cycle and the size of voltage measures data collection using one-ring electrode array with 16 electrodes.
Table 2-1. Current drive patterns comparison chart
Current drive pattern Electrodes configuration Current Injection Cycles Voltage Measures per cycle Voltage Measures data collection size
Remarks
Adjacent or neighboring
Two-electrodes 16 13
Better sensitivity in the boundary.
Higher resolution.
Opposite or polar
Two-electrodes 8 13
Better distribution of the sensitivity.
Less sensitive to conductivity changes at the boundary.
Lower resolution.
Diagonal or cross
Two-electrodes 14 13
Better sensitivity over the complete region.
Trigonometric or adaptive
Four-electrodes 8 15
The two-electrodes setups are preferred over the four-electrodes because only one current injector is required and this simplifies the hardware implementation. From the three possible current drive patterns, the adjacent was selected for this thesis purposes due to its higher resolution and better sensitive in the boundary features that are the both preferred in order to have a better approach for the target of pinpointing the location of nerves in the forearm.
2.5 Conductivity
The base model conductivity is one of the main parameters required to pose the forward problem. As explained in [20], the conductivity is the ability of an electrolyte solution for conducting a current through an electrodes pair.
The electrolytes conductivity is affected by the number of ions present in the solution, the concentration of the electrolyte, migration of ions and temperature [21]. The greater the number of ions in the solution the greater is the conductance;. In general, the molar conductance of an electrolyte increases with decrease in concentration or increase in dilution;
When conducting a current through a solution, some Faradaic processes take place at the interface of the electrodes. These Faradaic processes as well as the polarization effects between the electrodes and the solution could be overcame by using AC currents in the order of 1 ~ 4 kHz that allows the current to be transmitted through the electrode interface into the background solution through no-Faradaic processes. The electrical impedance of electrolyte solutions is considered as resistive up until 1 MHz; therefore, for this analysis purposes, these solutions can be characterized as pure resistors [22].
The conductivity is the reciprocal of resistivity and is expressed as: (2.3)
2.6 Biological tissues electrical behavior review
The biological tissues are compound of cells, intracellular and extracellular fluids whose behavior under the influence of external electric fields is determinate by complex bioelectrical impedance with high resistance and low reactance components [4]. The conductive characteristics of body fluids provide the resistive component, whereas the cell membranes, acting as imperfect capacitors, contribute to a frequency-dependence and the results are dependent only on liquids outside the cells [5].
Then, the bioelectrical impedance is a function of tissue composition and frequency of the applied AC signal and it is defined as the opposition of biological tissue to the flow of that electric alternating current [23]. A simplified equivalent circuit of bioelectrical impedance is illustrated in Figure 2-6.
Figure 2-6. Equivalent circuit of bioelectrical impedance.
Where Rs corresponds to skin impedance, Rt to tissue resistance and C to tissue capacitance.
to frequency allowing the electrical currents to be slightly deflected at the cell membranes.
2.7 The human forearm
The forearm is the segment of the upper limb comprised between the elbow joint and the wrist. At an structural level, the forearm is compound by two long bones, along with numerous muscles, tendons and ligaments [24], arteries, veins, nerves, the fat subcutaneous layer and the skin.
A cross-section of the forearm showing all the structures mentioned above is depicted in the Figure 2-7. A different color has been used to identify the different structures.
Figure 2-7. Depiction of cross-section showing all the structures in forearm (Image reproduced from [2]).
The bone on the left is called radius and the one on the right, ulna. Both of them form a rotational joint that allows the forearm to turn, so that the palm of the hand faces up or down [2].
Regarding muscles, the forearm is divided into two fascial compartments, the ventromedial or flexor and the dorsolateral or extensor. The muscles are divided in these compartments as an anterior group compound by the flexors of the wrist and fingers, the pronators and the palmaris longus; and a posterior group which comprises the extensors of the wrist and fingers, the abductor, the brachioradialis and the supinator [25].
The forearm has also two main arteries called radial and ulnar. The radial goes down the radial side of the forearm to the wrist and the ulnar goes down the ulnar side of the forearm to the wrist. These arteries are divided into minor arteries and veins which supply with blood all the muscles in the forearm.
The different biological structures in forearm have different values of conductivity. The theoretical values that have being used throughout the progress of the project are shown in Table 2-2. As explained in [1], most of these conductivity values were calculated as the average of the ranges described in [26]; meanwhile the reference conductivity value for marrow bone and nerves were found in [27].
Table 2-2. Theoretical values of conductivity and relative permittivity for different tissues in the human forearm (from [26] and [27]).
Tissue Conductivity
(S/m)
Relative Permittivity (F/m)
Fat 0.03 5x106
Muscle Transversal 0.09 2x107
Muscle Longitudinal 0.55 3.3x106
Skin 0.1135 6x105
Bone Cortical 0.03 5.2x105
Bone Marrow 0.002 4.5x104
Blood 0.7 3x103
2.8 Image Reconstruction
EIT image reconstructions are used for determining the impedance in the interior of a domain, given simultaneous measurements from the surface potentials developed by a direct or alternating electric signal injected at the domain boundary. EIT image reconstructions are conducted from the surface potentials data collection by two different modalities either in the difference imaging (or dynamic) or static imaging [4]. If the image reconstruction process uses just one set of potential boundaries, then the reconstruction is static; otherwise, if reconstruction employs more than a data results taken at different times, the reconstruction is dynamic.
EIT procedures are considered as "non-linear ill-possed inverse problem". Non-linear due to its solution procedure is unstable being affected by small changes; ill-posed because there is either not a unique solution or not solutions at all; and inverse insomuch as the target is to be determined an unknown model from a given model input and the model behavior.
In the other hand, as exposed by [28], a problem is defined as well-posed if for all admissible data, a unique solution exists and depends continuously on the data. And it is defined as forward if a model output behavior can be calculated from the given model and its known input.
Figure 2-8. EIT image reconstruction process general diagram.
EIT image reconstruction are highly affected by errors in measurements due to large changes in impedance that imply only small changes in every single surface potentials [7]. The impedance changes depend on several factors as surface conductivity between the injection electrode and the detector electrode, electrodes position or SUT movements. In order to reduce these errors some iterative methods are employed during reconstruction to keep the error as smaller as possible.
The complete reconstruction process is depicted in Figure 2-9 where the above gray rectangle represents the inverse problem; and the below gray rectangle, the forward. The forward problem for this research was modeled and analyzed in [1] and [2] using COMSOL and EIDORS platforms respectively. The purpose of this thesis is to implement the electronic instrumentation to outline the inverse problem and obtain a reconstructed image from phantom using EIDORS.
EIDORS reconstruction platform was selected in previous stages of this research due it is an open source that provides algorithms for forward and inverse modeling for EIT applications. Also, having an active community and being supported and maintained constantly are part of its main advantages
Inverse Problem Sensitivity (or Jacobian)
matrix
Surface potentials data collection Forward Problem
Figure 2-9. EIT image reconstruction process using an iterative algorithm diagram (Adapted from [30]).
2.8.1 Mathematical principles
EIT is directed by Maxwell equations which describe the electric and magnetic fields behavior. The magnetic fields are not considered for the EIT mathematical analysis purposes due to bioimpedance is characterized as a pure resistor.
As explained by Lionheart et al [13], for a given domain closed and bounded subset of 3D space with a boundary , the domain conductivity is represented as ; the scalar potential as and the electric field (E) as .
(2.4)
For EIT image reconstruction the fundamental problem to solve is that the current cannot be forced to flow linearly in an inhomogeneous volume conductor [7]. Since any current is produced in the interior, the potential fields lie on the applied currents. In this way, the continuity Kirchhoff’s law is:
(2.5)
Assuming , (2.5) corresponds to Laplace's equation. Now, having gradient of (2.4):
(2.6)
And, the divergence of (2.5)
(2.7)
The Dirichlet and Neummann boundary conditions are required to solve the scalar potential for a given conductivity . The Dirichlet boundary condition (2.8) determines the surface potential and the Neummann defines the surface outwards current density.
(2.8)
(2.9)
where n is the outward unitary vector to .
In the other hand, the complete electrodes model (CEM) takes into consideration the electrodes contact impedance when converting the surface current density to surface potentials. The mathematical setting for this model is described by Lionheart et al in [13].
Being electrodes` array conformed by electrodes (where ), the contact surface for each electrode is denoted as and the current and voltage for a specific electrode with respect to a reference as and , respectively. If the electrodes are considered as perfect conductors, it can be assumed that the voltage drop in each electrode is a constant and not current flows between the electrodes gap; then, the Dirichlet and Neummann boundary conditions are represented as:
(2.10)
(2.11)
In this way, the currents through the electrodes correspond to:
(2.12)
Then, considering (2.5) and the boundary conditions as in (2.10) and (2.11), the problem can be posed as:
(2.13)
(2.14)
(2.15)
where and .
(2.16)
where is presupposed the same for all the electrodes in the array.
As summary, the CEM is described by equations (2.13), (2.14) and (2.16).
2.8.2 Forward Problem
A problem is defined as forward when the behavior or output data of the system under test model can be predicted for a given input using physical theory linking the parameters of the model to the parameters being measured [31]. The diagram in Figure 2-10 illustrates the forward problem concept.
Figure 2-10. Forward problem diagram.
Regarding EIT applications, the forward problem, which is described using Maxwell’s equations, solves for a given conductivity distribution, the measured voltages under a known injection current. These resulting voltages are used for determining the values in the Jacobian matrix. The Jacobian matrix can be thought of as the differential change in each member of a given small perturbation of each [10].
The forward problem can be expressed as:
(2.17)
where corresponds to internal conductivity always greater than 0; to voltage measured in the boundary and is equivalent to the 1/2 order Sobolev space, so that the dissipated power within the domain is finite.
Now, being the forward problem a non-lineal problem it is represented as:
(2.18)
Input
Model
Parameters
where h is a non-lineal forward operator that can be determined using analytic methods just if the SUT correspond to a simple shape as a cylinder and has a homogeneous conductivity. For complex geometries and conductivities distributions, as is the case of the forearm, is required to discretize the geometry and conductivity using numerical methods techniques.
As explained in [15],Finite Element Method (FEM) is one of the possible ways of calculating the EIT forward problem for situations of complex geometry. This method divides the geometry under study into smaller elements like spheres or triangles. Then, all these small elements are reconnected forming “nodes” to maintain them together and define a set of algebraic equations. A large number of nodes and elements are required to accurately simulate the forward solution including the level of noise in real data.
2.8.3 Inverse Problem
In contrast to the forward problem, a problem is defined as inverse when the parameters of the system under test model can be inferred from the model behavior under a given input. The diagram in Figure 2-10 illustrates the forward problem concept.
Figure 2-11. Inverse problem diagram.
And, opposed to equation (2.17), the inverse problem can be expressed as:
(2.19)
Input Model
Parameters
being the known voltages measured in the boundary and corresponds to internal conductivity to be determined.
However, the problem in (2.19) is ill-posed; therefore, in order to convert it in a well-posed problem, it is required to apply regularization techniques [6] using a suitable regularization parameter (λ). Regularization in inverse problems not only decreases the ill-posed characteristics of the inverse matrix but it also improves the reconstructed image quality [32]. On the other hand, although regularization techniques contribute to obtain an approach to true impedance distribution, the spatial resolution is degraded.
2.9 Clinical applications
Electrical impedance tomography (EIT) imaging was firstly developed to be used in geological studies approximately 70 years ago [8]. Nowadays this technique has been employed in fields such as medical imaging, environmental sciences and nondestructive testing of materials besides geological exploration [33].
Regarding medical imaging, the first EIT device named "Sheffield Mark I" was developed by David C. Barber and Brian H. Brown in the early 1980s and it was used for reconstructing the first images of pulmonary function using a simple back-projection algorithm [8]. Hereafter, several possible applications in medicine were suggested, ranging from gastric emptying to brain function monitoring and from breast imaging to lung function assessment [23], being the ventilation and changes of end-expiratory lung volume the more widely investigated and developed application.
Drägerwerk and Swisstom AG are two companies investing in research to become a leading provider of life saving and non-invasive safety medical technology for patient monitoring
lung volume [23]. PulmoVista 500 offers an alternative to the well-established radiological techniques and conventional pulmonary monitoring that allows he lung function visualization at the bedside for evaluating the effects of lung pathologies on regional ventilation distribution. Since PulmoVista 500 can present the recollected information as trend data, it is possible to observe the evolution of the patient diseased under test before, during and after therapeutic interventions.
Other company interested on becoming a leader provider of life saving and non-invasive medical technology for patient monitoring is Swisstom AG. A Swiss company that has been developing medical devices driven by EIT for the monitoring of lung and heart function in intensive care patients and patients undergoing general anesthesia. They have recently developed Swisstom BB2, an imaging method that provides, similar than Pulmovista 500, real-time information about regional ventilation. Due to the safety features of EIT, Swisstom BB2 can be used continuously and it is suited for monitoring treatment effects in real-time directly at the patient´s bedside. Instead of plotting dynamic images, characteristic features can be extracted from these images and displayed as numbers or indices on a breath wise basis [34]. It requires a 32 electrodes array to inject the current and measure the voltages in the patient under test.
In the other hand, the University College London (UCL) EIT research group has adapted existing EIT designs for the demanding application of imaging changes in the brain due to conditions like stroke, epilepsy or normal physiological brain activity [35].
Chapter 3
Evaluation of the scope and limitations of previous implementations
developed for EIT systems
This chapter summarizes the results of assessment and comparison of designed or proposed EIT systems state of art that have been considered in order to define EIT for human forearm prototype requirements. The following sections describe the results for the components qualitative evaluation for the different criteria which are considered for the evaluation of the scope and limitations of other solutions and the review of electronic instrumentation that have been employed for previous EIT system assemblies found in literature.
3.1 EIT system component qualitative evaluation
The purpose of this evaluation is to make a comparison of some qualitative variables in an EIT system that helps to define the requirement of the prototype being implemented. These variables, conceptualization and expected values are described in Table 3-1.
Table 3-1. EIT system main components qualitative evaluation criteria
Variable Conceptualization Possible Values
Electrodes array size Minimum quantity of electrodes in the
array for better performance.
8, 16, 32
Current drive and voltage
measurements patterns
There are several patterns for current
drive and voltage measurements, but
four are the most used.
Adjacent
Opposite
Diagonal
Trigonometric
Current sources type Maximum quantity of current sources
determined according to the current
drive pattern being used.
Single
Variable Conceptualization Possible Values
Possible different voltage
measurements
Maximum quantity of different voltage
measurements that can be obtained
according to electrodes array size (N).
Adjacent:
Opposite:
Diagonal:
Trigonometric:
Signal to Noise ratio Minimum recommended signal to noise
ratio for reconstructing a readable
image.
Relative quantity
Sensitivity to uncertainties Level of sensitivity in the border and the
complete region of SUT due position
changes, involuntary movement or
noise.
1: Extremely sensitive
2: Quite sensitive
3: Moderately sensitive
4: Slightly sensitive
5: Negligible
Reconstruction type Level of precision of reconstructed
image.
Defined
Moderately defined
Diffuse
Table 3-2. List of articles and thesis documents collection reviewed for EIT system components qualitative evaluation
Item Article or thesis title Bibliography
reference
1 Finite Element Method Simulation Study of Electrical Impedance Tomography (EIT) for
the Human Forearm
[1]
2 Electrical Impedance Tomography (EIT) Image reconstruction for the Human Forearm [2]
3 Tomo: Wearable, Low-Cost, Electrical Impedance Tomography for Hand Gesture
Recognition
[3]
4 Electrical Impedance Tomography for Imaging Tissue Electroporation [10]
5 Electrical Impedance Tomography: The Realization of Regional Ventilation Monitoring [23]
6 Performance evaluation of a digital electrical impedance tomography system [37]
7 Electrical Impedance Tomography of brain function [35]
8 Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT [38]
9 Evaluation of EIT system performance [39]
10 A FPGA-Based Broadband EIT System for Complex Bioimpedance Measurements -
Design and Performance Estimation
[40]
11 IMPETOM System Analysis and use of EIT for detecting the source of epileptic seizures [41]
12 Electrical Impedance Tomography (EIT) System for Radiation-Free Medical Imaging
Based on LabVIEW
