A Novel Algorithm For High-Resolution Magnetic Induction Tomography Based On Stacked Auto-Encoder For Biological Tissue Imaging

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging method that uses an excitation coil to generate time-varying magnetic fields in space and reconstruct the internal conductivity distribution based on the phase difference. In this study, a new MIT reconstruction algorithm was proposed for non-contact measurement and monitoring of the location of the anomaly in the biomedical object of interest. To reconstruct the distribution of electrical characteristics inside the biological tissue, this technique uses a stacked auto-encoder (SAE) neural network composed of a multi-layer automatic encoder. The location and reconstruction accuracy of the anomaly based on SAE and back-projection were compared, and a hemorrhagic stroke was simulated to verify the practicability of the proposed algorithm. The results showed that the relative error of reconstruction based on the SAE network algorithm reached 0.29%, which improved anomaly reconstruction accuracy and reduced the prediction time to 0.02 s. At the same time, the network was used for the reconstruction of hemorrhagic stroke in different locations, amounts, and shapes. Accordingly, the SAE neural network reconstruction algorithm proposed in this study, which can autonomously learn the non-linear relationship between input and output, can solve the defects of the traditional reconstruction algorithm, such as serious artifacts and complex calculations.