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A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography

Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
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Academic Editor: Yuling Yan
Bioengineering 2021, 8(12), 191; https://doi.org/10.3390/bioengineering8120191 (registering DOI)
Received: 9 November 2021 / Revised: 20 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
Dynamic lung imaging is a major application of Electrical Impedance Tomography (EIT) due to EIT’s exceptional temporal resolution, low cost and absence of radiation. EIT however lacks in spatial resolution and the image reconstruction is very sensitive to mismatches between the actual object’s and the reconstruction domain’s geometries, as well as to the signal noise. The non-linear nature of the reconstruction problem may also be a concern, since the lungs’ significant conductivity changes due to inhalation and exhalation. In this paper, a recently introduced method of moment is combined with a sparse Bayesian learning approach to address the non-linearity issue, provide robustness to the reconstruction problem and reduce image artefacts. To evaluate the proposed methodology, we construct three CT-based time-variant 3D thoracic structures including the basic thoracic tissues and considering 5 different breath states from end-expiration to end-inspiration. The Graz consensus reconstruction algorithm for EIT (GREIT), the correlation coefficient (CC), the root mean square error (RMSE) and the full-reference (FR) metrics are applied for the image quality assessment. Qualitative and quantitative comparison with traditional and more advanced reconstruction techniques reveals that the proposed method shows improved performance in the majority of cases and metrics. Finally, the approach is applied to single-breath online in-vivo data to qualitatively verify its applicability. View Full-Text
Keywords: electrical impedance tomography; method of moment; sparse Bayesian learning; inverse problem; lung imaging; image reconstruction electrical impedance tomography; method of moment; sparse Bayesian learning; inverse problem; lung imaging; image reconstruction
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MDPI and ACS Style

Dimas, C.; Alimisis, V.; Uzunoglu, N.; Sotiriadis, P.P. A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography. Bioengineering 2021, 8, 191. https://doi.org/10.3390/bioengineering8120191

AMA Style

Dimas C, Alimisis V, Uzunoglu N, Sotiriadis PP. A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography. Bioengineering. 2021; 8(12):191. https://doi.org/10.3390/bioengineering8120191

Chicago/Turabian Style

Dimas, Christos, Vassilis Alimisis, Nikolaos Uzunoglu, and Paul P. Sotiriadis 2021. "A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography" Bioengineering 8, no. 12: 191. https://doi.org/10.3390/bioengineering8120191

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