Multi-wave Electromagnetic-Acoustic Sensing and Imaging (eBook)

Fei Gao received his B.S. degree in electrical engineering from Xi'an Jiaotong University, Xi'an, China in 2009. He received his PhD degree in electrical and electronic engineering at Nanyang Technological University in 2015. He was a postdoctoral visiting scholar at Stanford University in 2015. He is now working as a research fellow and electromagnetic-ultrasound group leader in NTU. He will join ShanghaiTech University as an Assistant Professor in early 2017. His research interests include fundamental study and system development of thermoacoustic and photoacoustic imaging modalities, circuit and system for biomedical applications. He has authored and co-authored about 50 journal and conference papers, one book chapter, and filed two patents.

Supervisor’s Foreword 6
Parts of this thesis have been published in the following journal articles: 8
Acknowledgements 9
Contents 10
List of Figures 13
List of Tables 22
Summary 23
1 Multi-wave EM-Acoustic Introduction 25
1.1 Background 25
1.1.1 Single-Wave Sensing and Imaging 25
1.1.1.1 Optical Imaging 26
1.1.1.2 Microwave Imaging 26
1.1.1.3 Ultrasound Imaging 26
1.1.1.4 Other Kinds of Single-Wave Imaging 26
1.1.2 Multi-wave Sensing and Imaging 27
1.1.2.1 Light-Induced Thermoacoustic Imaging (Photoacoustic Imaging) 27
1.1.2.2 Microwave-Induced Thermoacoustic Imaging 28
1.1.2.3 Magnetically Medicated Thermoacoustic Imaging 28
1.1.2.4 Other Kinds of Multi-wave Imaging 28
1.2 Research Motivation 28
1.3 Major Contribution 29
References 30
2 Multi-wave EM-Acoustic Methods 32
2.1 Circuit Modeling of EM-Acoustic Interaction 32
2.1.1 Motivation 33
2.1.2 Circuit Model of Microwave-Acoustic Interaction with Tumor Tissue 33
2.1.2.1 Microwave Scattering 34
2.1.2.2 EM Energy Absorption, Tissue Heating and Expansion 36
2.1.2.3 Tumor Vibration and Acoustic Generation 37
2.1.2.4 Acoustic Reflection 39
2.1.3 Characteristic Gain of Microwave-Acoustic Imaging 40
2.1.3.1 Pseudo S-parameter Extraction 40
2.1.3.2 Complete Circuit Model 42
2.1.3.3 Transducer Gain as Characteristic Gain 43
2.1.4 Simulation 44
2.1.5 Experimental Verification 48
2.1.6 2D Circuit Network Modeling for Heterogeneous Scenarios 51
2.1.6.1 Source Unit 51
2.1.6.2 Acoustic Channel 53
2.1.6.3 Acoustic Scatterer 54
2.1.7 2D Simulation Comparison 54
2.1.7.1 One Tumor Case 56
2.1.7.2 Two Tumor Case 56
2.1.7.3 Acoustic Scattering Case 56
2.1.8 Discussion and Conclusion 57
2.2 EM-Acoustic Phasoscopy Sensing and Imaging 60
2.2.1 Microwave-Acoustic Phasoscopy for Tissue Characterization 60
2.2.2 Photoacoustic Phasoscopy Super-Contrast Imaging 67
2.3 EM-Acoustic Resonance Effect and Characterization 71
2.3.1 Thermoacoustic Resonance Effect and Circuit Modeling 71
2.3.2 Photoacoustic Resonance Spectroscopy for Biological Tissue Characterization 78
2.4 EM-Acoustic Elastic Oscillation and Characterization 85
2.4.1 Introduction 85
2.4.2 Theory 86
2.4.3 Simulation and Experimental Results 90
2.4.4 Summary 94
2.5 Coherent EM-Acoustic Ultrasound Correlation and Imaging 94
2.5.1 Introduction 95
2.5.2 Theory 95
2.5.3 Experimental Setup 98
2.5.4 Results 100
2.5.4.1 System Evaluation 100
2.5.4.2 Signal SNR Improvement 100
2.5.4.3 Image of Vessel-Mimicking Phantom 102
2.5.4.4 Image of Vessel-Mimicking Phantom with Random Scatterer 104
2.5.4.5 Image of Vessel-Mimicking Phantom with High Resolution Ultrasound Imaging 105
2.5.5 Discussion and Conclusion 105
2.6 Micro-Doppler EM-Acoustic Effect and Detection 107
2.6.1 Introduction 107
2.6.2 Method and Preliminary Results 108
2.6.3 Discussion and Conclusion 113
References 113
3 Multi-wave EM-Acoustic Applications 117
3.1 Correlated Microwave-Acoustic Imaging for Breast Cancer Detection 117
3.1.1 Introduction 118
3.1.2 Theory 118
3.1.2.1 System Configuration 118
3.1.2.2 Proposed CMAI Method 120
3.1.3 Results 121
3.1.3.1 UWB Transmitter Design 121
3.1.3.2 Numerical Simulation 123
3.1.4 Conclusion 125
3.2 Single-Wavelength Blood Oxygen Saturation Detection 126
3.2.1 Introduction 126
3.2.2 Theory 128
3.2.3 Experimental Results 129
3.2.4 Discussion and Conclusion 131
3.3 Photoacoustic-Guided Depth-Resolved Raman Spectroscopy for Skin Cancer Detection 132
3.3.1 Theory 132
3.3.2 Preliminary Results 134
3.3.2.1 Phantom Preparation 134
3.3.2.2 Experimental Setup 134
3.3.2.3 Experimental Results 136
3.3.2.4 Experimental Results Using Single Laser Source 137
3.3.3 Discussion and Conclusion 138
3.4 Multistatic Photoacoustic Classification of Tumor Malignancy 139
3.4.1 Introduction 139
3.4.2 Methods 140
3.4.2.1 Tumor Malignancy Model 140
3.4.2.2 Photoacoustic Numerical Simulation 141
3.4.2.3 Multistatic Photoacoustic Classification 142
3.4.3 Simulation Results 144
3.4.3.1 Case 1: Full Field-of-View with 20 Sensors 144
3.4.3.2 Case 2: Half Field-of-View with 10 Sensors 144
3.4.3.3 Case 3: Non-centred Tumor Classification 146
3.4.4 Conclusion 148
References 148
4 Multi-wave EM-Acoustic Systems 150
4.1 NTU Photoacoustic Microscopy System for 3D Imaging 150
4.1.1 System Overview 150
4.1.2 System Specifications 152
4.1.3 System Operation Procedure 152
4.1.4 Photoacoustic Imaging in 3D: A Phantom Study 152
4.2 Multi-channel EM-Acoustic Imaging System 154
4.2.1 Multi-channel Microwave-Acoustic Imaging System Design 154
4.2.2 Multi-channel Photoacoustic Imaging Prototyping 155
4.3 Miniaturized Photoacoustic Receiver in Palm 157
4.3.1 Introduction 157
4.3.2 System Design and Development 157
4.3.3 Measurement Results 159
4.3.4 Discussion and Conclusion 161
5 Conclusion and Future Work 163
5.1 Conclusion 163
5.2 Future Work 164
Author’s Publications 166