Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To address these issues, we propose a spatial-frequency cross-fusion network (SFCF-Net) imaging algorithm, built on a multi-path fusion structure. This algorithm uses multi-path structures and hyper-dense connections to capture both spatial and frequency correlations between multi-frequency conductivity images, which achieves differential imaging for lesion targets of multiple categories through cross-fusion of information. According to both simulation and physical experiment results, the proposed SFCF-Net algorithm shows an excellent performance in terms of lesion imaging and category discrimination compared to the weighted frequency-difference, U-Net, and MMV-Net algorithms. The proposed algorithm enhances the ability of mfEIT to simultaneously obtain both structural and spectral information from the tissue being examined and improves the accuracy and reliability of mfEIT, opening new avenues for its application in clinical diagnostics and treatment monitoring.

中文翻译：

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SFCF-Net 中的多路径融合用于增强型多频电阻抗断层扫描


多频电阻抗断层扫描 (mfEIT) 提供了一种无损成像技术，可根据生物组织之间的阻抗谱差异重建对象内的电特性分布。然而，当背景组织的电导率依赖于频率时，该技术在对多类病变目标进行成像时面临挑战。为了解决这些问题，我们提出了一种基于多路径融合结构的空间频率交叉融合网络（SFCF-Net）成像算法。该算法利用多路径结构和超密集连接来捕获多频电导率图像之间的空间和频率相关性，通过信息的交叉融合实现多类别病变目标的差分成像。根据仿真和物理实验结果，与加权频差、U-Net 和 MMV-Net 算法相比，所提出的 SFCF-Net 算法在病变成像和类别区分方面表现出优异的性能。所提出的算法增强了mfEIT同时从被检查组织获取结构和光谱信息的能力，提高了mfEIT的准确性和可靠性，为其在临床诊断和治疗监测中的应用开辟了新途径。