An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition

BioMedical Engineering OnLine

Weichen Li¹  Feng Fu¹  Xuechao Liu¹  Hang Ma¹  Xiuzhen Dong¹  Haoting Li¹  Meng Dai¹  Canhua Xu¹  Xingwang Sun²  Ge Zhang³ 

[1] Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China;Department of Radiology, Bethune International Peace Hospital, Shijiazhuang, China;Department of Radiology, Bethune International Peace Hospital, Shijiazhuang, China;Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China;

关键词: Brain electrical impedance tomography;    Head movement;    Wavelet decomposition;    Gaussian distribution;    Brick-laying algorithm;   

DOI  :  10.1186/s12938-019-0668-8

来源: Springer

【 摘 要 】

BackgroundHead movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction.MethodsThis study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods.ResultsTo validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators’ increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images.ConclusionIn this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.

【 授权许可】

CC BY