Functional validation and comparison framework for EIT lung imaging.

<h4>Introduction</h4>Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such alg...

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Autores principales:	Bartłomiej Grychtol, Gunnar Elke, Patrick Meybohm, Norbert Weiler, Inéz Frerichs, Andy Adler
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/06a5d16fbac04d938ec6c8b9ee62ca95
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spelling	oai:doaj.org-article:06a5d16fbac04d938ec6c8b9ee62ca952021-11-25T06:05:25ZFunctional validation and comparison framework for EIT lung imaging.1932-620310.1371/journal.pone.0103045https://doaj.org/article/06a5d16fbac04d938ec6c8b9ee62ca952014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25110887/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Introduction</h4>Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.<h4>Methods</h4>We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.<h4>Results and conclusions</h4>Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.Bartłomiej GrychtolGunnar ElkePatrick MeybohmNorbert WeilerInéz FrerichsAndy AdlerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e103045 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bartłomiej Grychtol Gunnar Elke Patrick Meybohm Norbert Weiler Inéz Frerichs Andy Adler Functional validation and comparison framework for EIT lung imaging.
description	<h4>Introduction</h4>Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.<h4>Methods</h4>We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.<h4>Results and conclusions</h4>Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.
format	article
author	Bartłomiej Grychtol Gunnar Elke Patrick Meybohm Norbert Weiler Inéz Frerichs Andy Adler
author_facet	Bartłomiej Grychtol Gunnar Elke Patrick Meybohm Norbert Weiler Inéz Frerichs Andy Adler
author_sort	Bartłomiej Grychtol
title	Functional validation and comparison framework for EIT lung imaging.
title_short	Functional validation and comparison framework for EIT lung imaging.
title_full	Functional validation and comparison framework for EIT lung imaging.
title_fullStr	Functional validation and comparison framework for EIT lung imaging.
title_full_unstemmed	Functional validation and comparison framework for EIT lung imaging.
title_sort	functional validation and comparison framework for eit lung imaging.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/06a5d16fbac04d938ec6c8b9ee62ca95
work_keys_str_mv	AT bartłomiejgrychtol functionalvalidationandcomparisonframeworkforeitlungimaging AT gunnarelke functionalvalidationandcomparisonframeworkforeitlungimaging AT patrickmeybohm functionalvalidationandcomparisonframeworkforeitlungimaging AT norbertweiler functionalvalidationandcomparisonframeworkforeitlungimaging AT inezfrerichs functionalvalidationandcomparisonframeworkforeitlungimaging AT andyadler functionalvalidationandcomparisonframeworkforeitlungimaging
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Functional validation and comparison framework for EIT lung imaging.

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