Robust Preprocessing of Impulsive Motion Artifacts Using Low-Rank Matrix Recovery for Electrical Impedance Tomography

Abstract

Electrical impedance tomography (EIT) is a valuable bedside tool in critical care medicine and pneumology. However, artifacts associated with body and electrode movements, especially impulsive motion artifacts, hinder its routine use in clinical scenarios. Most of the existing algorithms for EIT data preprocessing or imaging cannot effectively address this issue. In this paper, we propose a novel method, namely, robust preprocessing for EIT (RP4EIT), to preprocess EIT boundary voltages using the concept of low-rank matrix recovery. It aims to resist impulsive motion artifacts and further to enhance the imaging quality. To attain good performance on both the normal measurements and contaminated data, we design a two-stage denoising algorithm using robust statistical analysis and low-rank recovery. Specifically, EIT boundary voltages are first formulated as a matrix, where the rows and columns correspond to the channels and frames, respectively. Then, the entries corrupted by impulsive noise of the matrix are identified and considered as missing elements. Subsequently, RP4EIT exploits the low-rank property to restore the missing components. In doing so, the impulsive motion artifacts are eliminated from EIT measurements. Furthermore, the convergence guarantee of RP4EIT is established. Experimental results on phantom and patient data demonstrate that RP4EIT is able to remove the impulsive motion artifacts from boundary voltages and the recovered data yield high-quality EIT images.