Engineering Tomography Lab (ETL), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK.
Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206 Lyon, France.
Sensors (Basel). 2018 Nov 17;18(11):4014. doi: 10.3390/s18114014.
Electrical resistance tomography (ERT) is an imaging technique to recover the conductivity distribution with boundary measurements via attached electrodes. There are a wide range of applications using ERT for image reconstruction or parameter calculation due to high speed data collection, low cost, and the advantages of being non-invasive and portable. Although ERT is considered a high temporal resolution method, a temporally regularized method can greatly enhance such a temporal resolution compared to frame-by-frame reconstruction. In some of the cases, especially in the industrial applications, dynamic movement of an object is critical. In practice, it is desirable for monitoring and controlling the dynamic process. ERT can determine the spatial conductivity distribution based on previous work, and ERT potentially shows good performance in exploiting temporal information as well. Many ERT algorithms reconstruct images frame by frame, which is not optimal and would assume that the target is static during collection of each data frame, which is inconsistent with the real case. Although spatiotemporal-based algorithms can account for the temporal effect of dynamic movement and can generate better results, there is not that much work aimed at analyzing the performance in the time domain. In this paper, we discuss the performance of a novel spatiotemporal total variation (STTV) algorithm in both the spatial and temporal domain, and Temporal One-Step Tikhonov-based algorithms were also employed for comparison. The experimental results show that the STTV has a faster response time for temporal variation of the moving object. This robust time response can contribute to a much better control process which is the main aim of the new generation of process tomography systems.
电阻抗断层成像（ERT）是一种通过附着在物体表面的电极来获取边界测量数据并重建电导率分布的成像技术。由于其具有高速数据采集、低成本、非侵入式和便携等优点，ERT 在图像重建或参数计算方面有着广泛的应用。尽管 ERT 被认为是一种具有高时间分辨率的方法，但与逐帧重建相比，时间正则化方法可以大大提高时间分辨率。在某些情况下，特别是在工业应用中，物体的动态运动是至关重要的。在实际应用中，人们希望对动态过程进行监测和控制。ERT 可以根据先前的工作确定空间电导率分布，并且 ERT 有可能在利用时间信息方面表现出良好的性能。许多 ERT 算法逐帧重建图像，这不是最优的，并且假设目标在采集每个数据帧时是静态的，这与实际情况不一致。尽管基于时空的算法可以考虑动态运动的时间效应，并可以生成更好的结果，但并没有太多的工作旨在分析时域中的性能。在本文中，我们讨论了一种新颖的时空全变分（STTV）算法在空间和时间域中的性能，并对基于时间一步 Tikhonov 的算法进行了比较。实验结果表明，STTV 对运动物体的时间变化具有更快的响应时间。这种稳健的时间响应可以为新一代过程层析成像系统的主要目标——更好的控制过程做出贡献。
