Beneficial techniques for spatio-temporal imaging in electrical impedance tomography. (29th June 2020)

• Record Type: Journal Article
• Authors: Boyle, Alistair; Aristovich, Kirill; Adler, Andy
• Is Part Of: Physiological measurement. Volume 41:Number 6(2020)
• Publication Date: 2020-06-29
• Subjects: electrical impedance tomography -- electrical resistivity tomography -- spatio-temporal -- kalman filters -- inverse problems -- performance evaluation -- Gauss–Newton; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ab8ccd
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective : 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. Approach : 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. Main results : 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. Significance : 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.