LAUSR

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