  While Electrical Impedance Tomography (EIT) has found many biomedicine
applications, a better resolution is needed to provide quantitative analysis
for tissue engineering and regenerative medicine. This paper proposes an
impedance-optical dual-modal imaging framework, which is mainly aimed at
high-quality 3D cell culture imaging and can be extended to other tissue
engineering applications. The framework comprises three components, i.e., an
impedance-optical dual-modal sensor, the guidance image processing algorithm,
and a deep learning model named multi-scale feature cross fusion network
(MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT
measurement and a binary mask image generated by the guidance image processing
algorithm, whose input is an RGB microscopic image. The network then
effectively fuses the information from the two different imaging modalities and
generates the final conductivity image. We assess the performance of the
proposed dual-modal framework by numerical simulation and MCF-7 cell imaging
experiments. The results show that the proposed method could significantly
improve image quality, indicating that impedance-optical joint imaging has the
potential to reveal the structural and functional information of tissue-level
targets simultaneously.
