N. Seifnaraghi et al., "Model Selection Based Algorithm in Neonatal Chest EIT," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 9, pp. 2752-2763, Sept. 2021, doi: 10.1109/TBME.2021.3053463
Model selection based algorithm in neonatal chest EIT
|Author:||Seifnaraghi, Nima1; de Gelidi, Serena2; Nordebo, Sven3;|
1Department of Natural Sciences, Middlesex University, London, U.K.
2Department of Natural Sciences, Middlesex University, U.K
3Department of Physics and Electrical Engineering, Linnaeus University, Sweden
4PEDEGO Research Unit, Medical Research Center, University of Oulu, Finland and Department of Children and Adolescents, Oulu University Hospital, Finland
5Department of Anesthesiology and Intensive Care Medicine, University Medical Centre Schleswig-Holstein, Germany
6Research Unit of Medical Imaging, Physics and Technology, University of Oulu and Oulu University Hospital, Finland
7Department of Electrical and Computer Engineering, University of Cyprus, Cyprus
8Department of Neonatology, Amsterdam University Medical Centres, The Neherlands
9Department of Radiology, Medical University of Graz, Austria
10Department of Electronic and Electrical Engineering, University College London, U.K
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021122162467
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-12-21
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.
IEEE transactions on bio-medical engineering
|Pages:||2752 - 2763|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
3123 Gynaecology and paediatrics
3121 General medicine, internal medicine and other clinical medicine
This work was supported in part by the CRADL project (http://cradlproject.org) funded by the European Union’s Horizon 2020 Research and Innovation Programme 2014-2018 under grant agreement no 668259, and in part by the Engineering and Physical Sciences Research Council (EPSRC) under grant no. EP/T001259.
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