Model selection based algorithm in neonatal Chest EIT

Seifnaraghi, Nima ORCID: https://orcid.org/0000-0002-6431-2404, de Gelidi, Serena ORCID: https://orcid.org/0000-0001-6141-2736, Kallio, Merja, Nordebo, Sven, Suo-Palosaari, Maria, Frerichs, Inéz, Sorantin, Erich, H. van Kaam, Anton, Sophocleous, Louiza, Tizzard, Andrew ORCID: https://orcid.org/0000-0002-6159-4901, Demosthenous, Andreas and Bayford, Richard ORCID: https://orcid.org/0000-0001-8863-6385 (2021) Model selection based algorithm in neonatal Chest EIT. IEEE Transactions on Biomedical Engineering . ISSN 0018-9294 [Article] (Published online first) (doi:10.1109/TBME.2021.3053463)

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Abstract

This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients.

The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by
considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated.

The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions.

Item Type: Article
Research Areas: A. > School of Science and Technology > Natural Sciences > Biophysics and Bioengineering group
Item ID: 31861
Notes on copyright: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
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Depositing User: Nima Seifnaraghi
Date Deposited: 21 Jan 2021 09:53
Last Modified: 09 Jun 2021 13:56
URI: https://eprints.mdx.ac.uk/id/eprint/31861

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