Interaction between Waves and the Human Body: Modeling and Detection

Date of Completion

January 2011


Geophysics|Engineering, Biomedical|Engineering, Civil




This Ph.D. dissertation focuses on the study of biomedical engineering applications based on the interaction between microwave/ultrasound and human body. It mainly discusses two novel techniques on human body diagnosis and detection including: transcranial thermoacoustic tomography (TAT) and bio-radiolocation using ultra wide band (UWB) impulse radar. Methodology development, numerical modeling and laboratory experiments study both techniques. By choosing promising imaging and data processing approaches, both techniques are proved with significant potential on practical use. ^ This thesis starts with a discussion on a series of different numerical methods for modeling wave propagation inside human body with high accuracy and efficiency. The methods include finite differences time domain (FDTD) methods, pseudo-spectral time domain (PSTD) methods, alternating direction implicit (ADI) FDTD/PSTD with staggered/non staggered grid, multi-region modeling technique and graphic processing unit (GPU) based high-speed computation. Combined with the review of physical properties in selected tissues of human body, these methods are partially applied to the study of two biomedical engineering applications. ^ The first biomedical engineering application described is TAT, a novel, non-invasive medical imaging. In this dissertation two imaging methods: Kirchhoff migration (KM) and reverse-time migration (RTM) are discussed for transcranial TAT. Their imaging qualities are verified and compared based on both synthetic and experimental data. RTM is proved as a promising approach that have better velocity variance and imaging quality on transcranial TAT imaging. ^ The second application is bio-radiolocation using ultra wide band (UWB) impulse radar with the purpose of picking up the cardio-respiratory signatures of human beings in complex environment. Signals from both the numerical simulations and laboratory experiments are processed with both FFT and Hilbert-Huang Transform (HHT), a novel signal processing approach for nonlinear and non-stationary data analysis. Results show that by using the FFT and HHT, human respiration characteristics can be successfully identified and differentiated for different subjects and a variety of respiratory statuses. ^