The Forward and Inverse Problem Based on Magneto-Acoustic Tomography with Current Injection
- 1 Department of Engineering Electromagnetics, Applications Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
- 2 Department of Engineering Electromagnetics, Applications Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
- 3 University of Chinese Academy of Sciences, Beijing, China
- 4 University of Chinese Academy of Sciences, Beijing, China
- 5 University of Chinese Academy of Sciences, Beijing, China
- 6 Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
Abstract
The Magneto-acoustic Tomography with Current Injection (MAT-CI) is a new biological electrical impedance imaging technique that combines Electrical Impedance Tomography (EIT) with Ultrasonic Imaging (UI), which possesses the non-invasive and high-contrast of the EIT and the high-resolution of the UI. The MAT-CI is expected to acquire high quality image and embraces a wide application. Its principle is to put the conductive sample in the Static Magnetic Field(SMF) and inject a time-varying current, during which the SMF and the current interact and generate the Lorentz Force that inspire ultrasonic signal received by the ultrasonic transducers positioned around the sample. And then according to related reconstruction algorithm and ultrasonic signal, electrical conductivity image is obtained. In this paper, a forward problem mathematical model of the MAT-CI has been set up to deduce the theoretical equation of the electromagnetic field and solve the sound source distribution by Green’s function. Secondly, a sound field restoration by Wiener filtering and reconstruction of current density by time-rotating method have deduced the Laplace’s equation that caters to the current density to further acquire the electrical conductivity distribution image of the sample through iteration method. In the end, double-loop coils experiments have been conducted to verify its feasibility.
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