Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China.
Sci Rep. 2018 Jul 4;8(1):10086. doi: 10.1038/s41598-018-28284-2.
Dynamic electrical impedance tomography (EIT) promises to be a valuable technique for monitoring the development of brain injury. But in practical long-term monitoring, noise and interferences may cause insufficient image quality. To help unveil intracranial conductivity changes, signal processing methods were introduced to improve EIT data quality and algorithms were optimized to be more robust. However, gains for EIT image reconstruction can be significantly increased if we combine the two techniques properly. The basic idea is to apply the priori information in algorithm to help de-noise EIT data and use signal processing to optimize algorithm. First, we process EIT data with principal component analysis (PCA) and reconstruct an initial CT-EIT image. Then, as the priori that changes in scalp and skull domains are unwanted, we eliminate their corresponding boundary voltages from data sets. After the two-step denoising process, we finally re-select a local optimal regularization parameter and accomplish the reconstruction. To evaluate performances of the signal processing-priori information based reconstruction (SPR) method, we conducted simulation and in-vivo experiments. The results showed SPR could improve brain EIT image quality and recover the intracranial perturbations from certain bad measurements, while for some measurement data the generic reconstruction method failed.
动态电阻抗断层成像（EIT）有望成为监测脑损伤发展的一项有价值的技术。但在实际的长期监测中，噪声和干扰可能会导致图像质量不足。为了帮助揭示颅内电导率的变化，引入了信号处理方法来提高 EIT 数据的质量，并优化算法使其更具鲁棒性。然而，如果我们能够正确地将这两种技术结合起来，EIT 图像重建的增益可以显著提高。基本思想是在算法中应用先验信息来帮助 EIT 数据去噪，并使用信号处理来优化算法。首先，我们使用主成分分析（PCA）对 EIT 数据进行处理，并重建初始 CT-EIT 图像。然后，由于头皮和颅骨区域的变化是不希望的，我们从数据集消除它们对应的边界电压。在两步去噪过程之后，我们最终重新选择局部最优正则化参数并完成重建。为了评估基于信号处理-先验信息的重建（SPR）方法的性能，我们进行了模拟和体内实验。结果表明，SPR 可以提高脑 EIT 图像质量，并从某些不良测量中恢复颅内扰动，而对于某些测量数据，通用重建方法失败。
