Multimodal Image Reconstruction of Electrical Impedance Tomography Using Kernel Method

The inverse problem of electrical impedance tomography (EIT) is nonlinear and severely ill-posed, resulting in low image quality, which explicitly involves the aspects structure preservation conductivity contrast differentiation. This article reports a kernel method-based multimodal EIT reconstruction approach to tackle this challenge. method performs image-level segmentation-free information fusion incorporates structural an auxiliary high-resolution into inversion process through matrix, leading unconstrained least square problem. We describe general way so that images from various imaging modalities can be adopted as if they contain sufficient information. Compared with some state-of-the-art algorithms, proposed generates superior on challenging simulation experimental phantoms. It also presents advantage suppressing interference imaging-irrelevant objects image. Simulation experiment results suggest has great potential applied more complex tissue engineering applications.