The Study on the Inverse Problem of Applied Current Thermo-Acoustic Imaging Based on Generative Adversarial Network

Abstract

Applied current thermo-acoustic imaging (ACTAI) has a prospect applications in medical imaging, with the advantages of high contrast of ecclectrical impedance tomography and high spatial resolution in ultrasound imaging. Although the imaging modality has high excitation efficiency and signal-to-noise ratio, there is still a great challenge on high resolution reconstruction of conductivity under low frequency electromagnetic excitation. In this paper, a new method for reconstructing the conductivity based on generative adversarial network is proposed. The proposed algorithm consists of the following three main steps: firstly, by using Wiener filtering deconvolution method, the original acoustic fields on the detected boundary are reconstructed by the acoustic signals originated from the ultrasonic transducers. And then the initial acoustic source image is obtained by the filtering back projection method. Finally, the initial acoustic source image are used as training samples and labels of a deep learning network, which is designed for the conductivity reconstruction. Theoretical analysis show that the method proposed in this paper can solve the inverse problem of the conductivity reconstruction by the machine learning models and obtain the accurate and stable images. This provides a new idea for solving the problem of conductivity reconstruction in the applied current thermo-acoustic imaging.