School of Human Kinetics, University of Ottawa, Ottawa, Canada.
Physiol Meas. 2020 Jul 2;41(6):064003. doi: 10.1088/1361-6579/ab8ccd.
Electrical impedance tomography (EIT) typically reconstructs individual images from electrical voltage measurements at pairs of electrodes due to current driven through other electrode pairs on a body. EIT images have low spatial resolution, but excellent temporal resolution. There are four methods for integrating temporal data into an EIT reconstruction: filtering over measurements, filtering over images, combined spatial and temporal (spatio-temporal) regularization, and Kalman filtering. These spatio-temporal methods have not been directly compared, making it difficult to evaluate relative performance and choose an appropriate method for particular use cases.
We (i) develop a common framework, (ii) develop comparison metrics, (iii) perform simulation and tank studies which directly compare algorithms, and (iv) report on relative advantages of the different algorithms.
Temporal filtering is well understood, but often not considered as part of the imaging process despite a direct impact on image reconstruction quality. Spatio-temporal regularized techniques are not yet efficient but offer tantalizing advantages. Kalman filtering enables adaptive filtering for time-varying measurement/image noise at the cost of often over-regularized (sub-optimal) images which can now be understood in the same framework as the other techniques. Further research into efficient implementations of Gauss-Newton spatio-temporal regularization will allow temporal and spatial covariance to be explicitly defined for longer time series (n > 10 frames) where temporal regularization can be more effective. For the immediate analysis of temporally varying images, we recommend the use of adaptive (time-varying) temporal filtering of measurements followed by adaptive spatial regularization (hyperparameter selection) as the most computationally efficient and effective approach currently available.
The analysis of variation within regions of an EIT image to extract physiological measures (functional imaging), has become an important EIT technique where temporal and spatial aspects of analysis are tightly integrated. This work gives guidance on available methods and suggests directions for future research.
电阻抗断层成像（EIT）通常通过在身体上的其他电极对驱动电流，从一对电极的电压测量中重建单个图像。EIT 图像的空间分辨率低，但时间分辨率高。有四种将时间数据集成到 EIT 重建中的方法：在测量上滤波、在图像上滤波、时空正则化（时空正则化）和卡尔曼滤波。这些时空方法尚未直接比较，因此难以评估相对性能并为特定用例选择适当的方法。
我们（i）开发了一个通用框架，（ii）开发了比较指标，（iii）进行了直接比较算法的模拟和罐研究，以及（iv）报告了不同算法的相对优势。
时间滤波已经得到很好的理解，但尽管对图像重建质量有直接影响，但通常不作为成像过程的一部分来考虑。时空正则化技术还不是很有效，但提供了诱人的优势。卡尔曼滤波可以实现时变测量/图像噪声的自适应滤波，但代价是图像通常过度正则化（次优），现在可以在相同的框架中理解其他技术。进一步研究高斯-牛顿时空正则化的有效实现将允许为更长的时间序列（n>10 帧）显式定义时间和空间协方差，在时间正则化更有效。对于随时间变化的图像的即时分析，我们建议使用自适应（时变）测量时间滤波，然后进行自适应空间正则化（超参数选择），这是目前可用的最具计算效率和有效性的方法。
对 EIT 图像的区域内变化进行分析以提取生理测量值（功能成像）已经成为 EIT 的一项重要技术，其中分析的时间和空间方面紧密结合。这项工作为现有方法提供了指导，并为未来的研究提出了方向。
