Limits...
Functional validation and comparison framework for EIT lung imaging.

Grychtol B, Elke G, Meybohm P, Weiler N, Frerichs I, Adler A - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany; Fraunhofer Project Group for Automation in Medicine and Biotechnology, Mannheim, Germany.

ABSTRACT

Introduction: 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.

Methods: 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.

Results and conclusions: 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.

Show MeSH

Related in: MedlinePlus

Overview of the proposed methodology.Peep – positive end-expiratory pressure; FIo2 - fraction of oxygen in inspired gas; V – air volume; t – time; VT – tidal volume; CoV - centre of ventilation; FEM – finite element model.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4128601&req=5

pone-0103045-g001: Overview of the proposed methodology.Peep – positive end-expiratory pressure; FIo2 - fraction of oxygen in inspired gas; V – air volume; t – time; VT – tidal volume; CoV - centre of ventilation; FEM – finite element model.

Mentions: Since the potential role of EIT in ventilation therapy is to detect changes in the regional distribution of ventilation, our proposed validation framework is explicitly designed to test its ability to do so. An overview of the framework is presented in Fig. 1.


Functional validation and comparison framework for EIT lung imaging.

Grychtol B, Elke G, Meybohm P, Weiler N, Frerichs I, Adler A - PLoS ONE (2014)

Overview of the proposed methodology.Peep – positive end-expiratory pressure; FIo2 - fraction of oxygen in inspired gas; V – air volume; t – time; VT – tidal volume; CoV - centre of ventilation; FEM – finite element model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128601&req=5

pone-0103045-g001: Overview of the proposed methodology.Peep – positive end-expiratory pressure; FIo2 - fraction of oxygen in inspired gas; V – air volume; t – time; VT – tidal volume; CoV - centre of ventilation; FEM – finite element model.
Mentions: Since the potential role of EIT in ventilation therapy is to detect changes in the regional distribution of ventilation, our proposed validation framework is explicitly designed to test its ability to do so. An overview of the framework is presented in Fig. 1.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany; Fraunhofer Project Group for Automation in Medicine and Biotechnology, Mannheim, Germany.

ABSTRACT

Introduction: 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.

Methods: 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.

Results and conclusions: 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.

Show MeSH
Related in: MedlinePlus