An Approximate Point-Spread Function for Electrical Impedance Tomography Imaging

Abstract

In Electrical Impedance Tomography, the ill-posed nature of the inverse problem and the interaction between the electric current and objects cause poor spatial resolution and blurring in the reconstructed images. This study proposes a numerical method, called the voltage-dependent point-spread function, which depends on the initial setting of the environment, such as voltage, conductivity, permeability, and frequency, to overcome the above problems. When the object layout is convoluted with a point spread function, the Electrical Impedance Tomography image can be reconstructed regardless of the conditions in forward and inverse problems, which is suitable for applications requiring big data, such as deep learning or machine learning. The object’s size in the Electrical Impedance Tomography image can be reconstructed to approximately the real size by deconvolution with a point-spread function. The proposed method was validated using conventional methods through simulations and experimental experiments. The results demonstrated the feasibility and applicability of this method in clinical practice.

Keywords

• Approximate computing
• biomedical imaging
• convolution
• deconvolution
• electrical impedance tomography
• point-spread function