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Title Page

Abstract

Publications from this thesis

Contents

I. Introduction 14

1.1. Electrical Impedance Tomography 14

1.2. EIT System for Clinical Application 15

1.3. Clinical Imaging Method for Thermal Ablation 16

1.4. Electrical Property for EIT Imaging 17

1.5. Fabric Interface for Long-term Monitoring. 19

1.6. Research Objective and Dissertation Outline 20

II. KHU Mark2.5 EIT system 22

2.1. System Development 22

2.1.1. System Architecture 22

2.1.2. External Device Interface Unit 24

2.1.3. Impedance Measurement Module (IMM) 25

2.1.4. Analog Backplane and Electrode Interface Unit 27

2.1.5. Isolated DC Power Supply and System case 28

2.2. Calibration Method 30

2.2.1. Analog Circuit Design for Calibration 30

2.2.2. Automatic Self-Calibration 33

2.3. Operation Program 38

2.3.1. Pipeline Structure 38

2.3.2. PC Software Based on Script 40

2.4. Performance Evaluation 43

2.4.1. Basic performance 45

2.4.2. Automatic Calibration 45

2.4.3. 24 Hours Continuous Operation 49

2.4.4. Long-Term Evaluation of 1 Week Continuous Operation 49

III. RF ablation monitoring 51

3.1. Phantom Experiment 51

3.1.1. Experimental Configuration and System Calibration 51

3.1.2. Data Collection and Processing 53

3.1.3. Conductivity Spectrum 55

3.1.4. Time Series of Conductivity Images 56

3.1.5. Estimation of Ablation Area 58

3.2. Post mortem Experiment 60

3.3. in-vivo Experiment 60

IV. Ventilation and Heart beat monitoring with Fabric Interface. 64

4.1. Conductive Fabric Electrode Interface 64

4.1.1. Fabric Material Evaluation Method 64

4.1.2. Fabric Material Evaluation Results 70

4.1.3. Fabric Electrode Belt 76

4.2. Ventilation Monitoring during Sitting / Running 79

4.3. Animal Experiment; respiration and cardiac monitoring using ECG gated EIT 83

V. DISCUSSION AND CONCLUSION 86

5.1. KHU Mark 2.5 system 86

5.2. RF ablation monitoring 89

5.3. Fabric interface and ventilation/hear beat monitoring 91

VI. References 92

APPENDIX 99

List of Figures

Fig. 2.1: KHU Mark2.5 EIT system architecture. The IMM includes an independent constant... 23

Fig. 2.2: (a) The 16-channel KHU Mark2.5 EIT system and internal view of the KHU Mark2.5... 24

Fig. 2.3: Impedance measurement module (IMM) and its functional blocks. 26

Fig. 2.4: Schematic of the embedded current source calibrator using the droop method 26

Fig. 2.5: (a) System case for 16ch. (b) Top view of system case. (c) Power case for 16ch. (d)... 29

Fig. 2.6: Improved Howland current circuit with additional C and GICs. 30

Fig. 2.7: One generalized impedance converter within 4 GICs. 31

Fig. 2.8: Generated inductances by GIC considering the error of components and temperature... 32

Fig. 2.9: Output impedance changed by the in the HCP and in GIC when tested at 100 ㎑. 33

Fig. 2.10: Current source calibrator for the KHU Mark2 EIT system. 34

Fig. 2.11: Automatic self-calibration procedure in the KHU Mark2.5 EIT system. 35

Fig. 2.12: Steps of automatic self-calibration procedure. 36

Fig. 2.13: Typical pipeline structure of KHU Mark2 system when the lowest operating... 39

Fig. 2.14: Screen capture of the KHU Mark2.5 EIT system software for data acquisition and... 40

Fig. 2.15: (a) Example script file of 8-channel KHU Mark2.5 EIT system. (b) Example... 41

Fig. 2.16: SNR dependence on warm up following powering on of the system and following... 45

Fig. 2.17: (a) DC offset current before and after calibration. (b) Output impedance of 16... 47

Fig. 2.18: (a) Amplitude difference between source and sink current following CCS... 48

Fig. 2.19: (a) SNR and (b) reciprocity error for 24 hours operated at 1 ㎑. 49

Fig. 2.20: (a) SNR and (b) reciprocity error over days at 1 ㎑. 50

Fig. 3.1: Experimental configuration for monitoring of temperature distribution and tissue... 52

Fig. 3.2: Timing diagram for data acquisition. 53

Fig. 3.3: Conductivity spectrum of the liver tissue specimens measured at the P1 (ablation... 56

Fig. 3.4: Time difference (TD) and weighted frequency difference (wFD) conductivity images... 57

Fig. 3.5: (a) The output power of RF ablator and temperature variations at P1 (ablation region)... 58

Fig. 3.6: Time-difference (TD) and weighted frequency difference (wFD) contour map to... 59

Fig. 3.7: (a) Experiment environment for post mortem, (b) MR image for selecting the imaging... 60

Fig. 3.8: Experiment environment for in-vivo with ultra-sound guide and microwave ablation. 61

Fig. 3.9: EIT time course images from the first ablation in animal 2 (1min/image). The... 63

Fig. 3.10: EIT time course images from the three ablations in animal 3 (0.5min/image). 63

Fig. 4.1: (a) PEDOT coated PVDF nanofiber web electrode. (b) Ag plated PVDF nanofiber... 65

Fig. 4.2: (a, b) Experimental setup for three- and four-electrode contact impedance. (b) Step... 66

Fig. 4.3: Experimental setup for waveform fidelity measurements. 69

Fig. 4.4: Measured electrode contact resistances (filled circles) and capacitances (filled... 71

Fig. 4.5: Step response of each electrode to 120㎳ period, 50%duty cycle square wave. Data... 72

Fig. 4.6: Noise power spectral density for test electrodes and Ag/AgCl reference and (inset)... 73

Fig. 4.7: Noise standard deviation measured in each electrode. Data were normalized to... 74

Fig. 4.8: Waveform fidelity plotted for each electrode, referred to Ag/AgCl. 74

Fig. 4.9: Comparison of ECG measurement characteristics of (left) Ag plated PVDF nanofiber... 75

Fig. 4.10: Ag-plated PVDF nanofiber web (a) optical photograph; (b) SEM image. 76

Fig. 4.11: (a) Cross sectional view of electrode belt, (b) inner band and (c) outer band design... 78

Fig. 4.12: (a) Design of Ag-plated PVDF nanofiber web electrode and elastic band with eyelet... 79

Fig. 4.13: Customized electrode belt incorporating Ag-plated PVDF nanofiber web electrode. 80

Fig. 4.14: Contact impedances of Ag/AgCl electrodes and flexible electrode belt for EIT using... 81

Fig. 4.15: Pulmonary conductivity images during two different activities (sitting and running)... 82

Fig. 4.16: (a) MRI image slice of monitoring area. (b) Experimental setup and electrode... 83

Fig. 4.17: (a) pulmonary ventilation images, (b) relative conductivity change from... 84

Fig. 4.18: (a) ECG signal from MP35, (b) relative conductivity change from reconstructed... 85

초록보기

 Electrical Impedance Tomography (EIT) is a safe medical imaging technology, requiring no ionizing or heating radiation, as opposed to most other imaging modalities. This has led to a clinical interest in its use for long-term monitoring, possibly at the be10dside, for ventilation monitoring, bleeding detection, gastric emptying and epilepsy foci diagnosis. These long-term applications demand auto-calibration and high stability over long time periods.

To address this need we have developed a new multi-frequency EIT system called the KHU Mark2.5 with automatic self-calibration and cooperation with other devices via a timing signal for synchronization with other medical instruments. The impedance measurement module (IMM) for flexible configuration as a key component includes an independent constant current source, an independent differential voltmeter, and a current source calibrator, which allows automatic self-calibration of the current source within each IMM. We installed a resistor phantom inside the KHUMark2.5 EIT system for intra-channel and inter-channel calibrations of all voltmeters in multiple IMMs.

We show the deterioration of performance of an EIT system over time and the improvement due to automatic self-calibration. The system is able to maintain SNR of 80 dB for frequencies up to 250 kHz and below 0.5% reciprocity error over continuous operation for 24 hours. Automatic calibration at least every 3 days is shown to maintain SNR above 75 dB and reciprocity error below 0.7% over 7 days at 1 kHz. A clear degradation in performance results with increasing time between automatic calibrations allowing the tailoring of calibration to suit the performance requirements of each application.

To shows the new system’s ability in clinical issue, we applied the system to 1) RF ablation monitoring for liver, 2) ventilation/heart beat monitoring. We will introduce the needs for those application using EIT method and show preliminary results.

At first, to demonstrate the feasibility of time-difference and weighted frequency difference conductivity imaging to visualize real-time temperature distribution and ablation region estimation due to RF ablation. The electrical conductivity spectrum of biological tissue during RF ablation reflects the mobility of ions in intra- and extra-cellular fluids and changes in cellular morphology induced by heating. Time series multi-frequency conductivity data was measured in an ex-vivo bovine liver model by a high-speed electrical impedance tomography (EIT) system. The EIT system was protected by filters to suppress the RF energy from simultaneous RF ablation to allow interleaved real-time imaging. The conductivity variation at pixels in regions of interest were compared with temperature recordings. Contours of impedance change were visualized and compared to the ablation area.

Tetra-polar conductivity spectroscopy measurements confirmed an increase at all frequencies and a loss of frequency change associated with loss of cellular structure. Time difference images changed with heating during ablation and heat dissipation following the ablation. Weighted frequency difference images presented persistent changes due to the morphological change in the ablation zone. The size of the persistent conductivity change in the images was comparable to the visualized ablation region. We obtained real-time temperature distribution and ablation region detection separately when applying time and weighted frequency difference conductivity imaging in ex-vivo liver.

Second, we tried to monitoring lung ventilation and ECG gated heart beat monitoring. We improved the EIT system by introducing an EIT electrode interface consisting of nanoweb fabric electrodes and by adding a synchronized biosignal measurement system for gated conductivity imaging. ECG and respiration signals are collected to analyse the relationship between the changes in conductivity images and cardiac activity or respiration.

The biosignal measurement system provides a trigger to the EIT system to commence imaging and the EIT system produces an output trigger. This EIT acquisition time trigger signal will also allow us to operate the EIT system synchronously with other clinical devices. This type of biosignal gated conductivity imaging enables capture of fast cardiac events and may also improve images and the signal-to-noise ratio (SNR) by using signal averaging methods at the same point in cardiac or respiration cycles.

As an example we monitored the beat by beat cardiac-related change of conductivity in the EIT images obtained at a common state over multiple respiration cycles. We showed that the gated conductivity imaging method reveals cardiac perfusion changes in the heart region of the EIT images on a canine animal model. These changes appear to have the expected timing relationship to the ECG and ventilator settings that were used to control respiration. As EIT is radiation free and displays high timing resolution its ability to reveal perfusion changes may be of use in intensive care units for continuous monitoring of cardiopulmonary function.

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