Microwave-acoustic breast tumor detection and design and analysis of wireless implants for neurophysiological research

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The strength of my research group lies in accurate numerical modeling of interaction between electromagnetic waves and tissue. BREAST CANCER DETECTION WITH MICROWAVES is a novel approach to breast imaging that promises to complement existing diagnostic techniques. Due to a notable contrast in electrical properties of breast tumors with respect to the healthy surrounding tissue, the tumor is "visible" to a microwave signal. If such a signal is launched from the skin surface, it penetrates through the healthy tissue until it encounters the tumor. Then, part of the signal "backscatters", and part of it is absorbed by the tumor, causing the heating and thermal expansion of the tumor, which generates an acoustic wave. Both the backscatter and the acoustic signals can be captured at the skin surface. The novelty of the proposed research is development of: 1) a three-dimensional model that will couple the electromagnetic, thermal and acoustic processes ; 2) a data fusion algorithm, which will combine the signals obtained by the backscatter of the microwave and by acoustic signal generated by thermal expansion for image enhancement. WIRELESS IMPLANTABLE BIO-SENSORS will enable a researcher to acquire data from electrodes and biological sensors over an extended time-period while allowing the subject with the implant (e.g. mouse) to remain functional and retain a good quality of life. Currently there is no way to remotely (without wires) stimulate and acquire data from a variety of bio-sensors using implantable devices. Technical barriers are: a) Size: implants need to be small, yet electrically efficient, b) Lifetime - requires a battery-less power solution. c) Range beyond the current limitation of 0.5m range, d) Neurophysiology requires multiple channel high-bandwidth data collection, e) Safety: power-delivery management and potential field-hazard assessment. Any sensor prototype that can overcome these obstacles needs to include accurate simulations of in-vivo sensors. My research group will develop advanced numerical models of the in-tissue sensor and optimize its design with two goals: 1) low power deposition in the surrounding tissues and 2) reliability of signal transmission in the heterogeneous tissue.