Abstract
Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging.
Highlights
MethodThe implementation of the new method consists of the following steps: first, the electrical signal measured by the ultrasonic probe is preprocessed by Wiener filter d econvolution[18,19] to obtain the original acoustic signal emitted by the measured sample
Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target
The fundamental reason is that the Signal-to-Noise Ratio of the ultrasound signal is low, while the inverse problem of rebuilding the conductivity image from the ultrasound signal has a strong nonlinearity, which makes the reconstruction process greatly affected by noise, and the reconstruction results of conductivity cannot meet the requirements of functional imaging
Summary
The implementation of the new method consists of the following steps: first, the electrical signal measured by the ultrasonic probe is preprocessed by Wiener filter d econvolution[18,19] to obtain the original acoustic signal emitted by the measured sample. According to the ACTAI forward problem model, the thermal function in the imaging area can be calculated as H(r′ ) = f ∈ Rn×1 , and the thermal sound source reconstructed by filtered back projection is y ∈ Rn×1. The specific process is as follows: first, for each new conductivity sample, calculate the sound field distribution in the COMSOL software according to the multi-physics coupling positive problem model, and the measurement signal is obtained after convolution probe characteristics to solve the positive problem; secondly, the original sound signal is obtained by wiener filtering deconvolution and the sound source distribution is reconstructed through filtered back projection; use the reconstructed sound source as the input of the generator in the GAN network, the output of which is the conductivity image to be solved
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