IEEE Trans Med Imaging. 2022 Apr;41(4):983-996. doi: 10.1109/TMI.2021.3129739. Epub 2022 Apr 1.
While Electrical Impedance Tomography (EIT) has found many biomedicine applications, better image quality is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper reports an impedance-optical dual-modal imaging framework that primarily targets 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 improve the image quality notably, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.
虽然电阻抗断层成像（EIT）在许多生物医学应用中得到了广泛应用，但仍需要更好的图像质量来提供组织工程和再生医学的定量分析。本文报道了一种阻抗-光学双模态成像框架，该框架主要针对高质量的 3D 细胞培养成像，并可扩展到其他组织工程应用。该框架包括三个组件，即阻抗-光学双模态传感器、引导图像处理算法以及用于信息融合的深度学习模型，称为多尺度特征交叉融合网络（MSFCF-Net）。MSFCF-Net 有两个输入，即 EIT 测量值和由引导图像处理算法生成的二进制掩模图像，其输入是 RGB 显微镜图像。然后，网络有效地融合了来自两种不同成像模式的信息，并生成最终的电导率图像。我们通过数值模拟和 MCF-7 细胞成像实验评估了所提出的双模态框架的性能。结果表明，所提出的方法可以显著提高图像质量，表明阻抗-光学联合成像有可能同时揭示组织水平靶标的结构和功能信息。
