A Fidelity-Embedded Regularization Method for Robust Electrical Impedance Tomography

Abstract

Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or the Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Based on the analysis of the structure of the Jacobian matrix, we propose a fidelity-embedded regularization (FER) method and a motion artifact reduction filter. Incorporating the Jacobian matrix in the regularization process, the new FER method with the motion artifact reduction filter offers stable reconstructions of high-fidelity images from noisy data by taking a very large regularization parameter value. The proposed method showed practical merits in experimental studies of chest EIT imaging.

Feature Extraction of Upper Airway Dynamics during Sleep Apnea using Electrical Impedance Tomography

“…2(f)). The time-series images were reconstructed, by fidelity-embedded regularization (FER) algorithm, after fitting U UA to the original data size 18 . The conductivity change signal was computed as a sum of pixels then the upper airway closure signal (UA_closure) was expressed as the psercentage of conductivity changes.…”

A Learning-Based Method for Solving Ill-Posed Nonlinear Inverse Problems: A Simulation Study of Lung EIT

“…To this end, we preprocess the voltage measurements as in [39]. We extract the boundary error, denoted byV err , by using the boundary sensitive Jacobian matrix S bdry :V…”

“…Before closing this introduction section, we should mention that our method does not use ground-truth labeled data for training, because lung EIT lacks a known ground truth at present. Although we have collected many human experiment data using 16 channel EIT system [39], its ground truthiness is not clear from a clinical point of view. Phantom experimental results cannot be used for ground-truth data, which are far from realistic.…”

Electrical impedance tomography with multiplicative regularization

“…Linear methods usually linearize the forward model and reconstruct the conductivity changes between different time points. See [9,4,64,68,44,39,43] for some related work. Nonlinear methods can be further classified into two categories: iterative and direct (noniterative) methods.…”