Model Selection Based Algorithm in Neonatal Chest EIT

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 a...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2021-09, Vol.68 (9), p.2752-2763
Main Authors: Seifnaraghi, Nima, de Gelidi, Serena, Nordebo, Sven, Kallio, Merja, Frerichs, Inez, Tizzard, Andrew, Suo-Palosaari, Maria, Sophocleous, Louiza, van Kaam, Anton H., Sorantin, Erich, Demosthenous, Andreas, Bayford, Richard H.
Format: Article
Language:English
Subjects:
Algorithms
Computed tomography
Conditional probability
Conductivity
Decision making
Electrical impedance
Electrical impedance tomography
Electrodes
Electrotechnology
Elektroteknik alt Electrical engineering
Image processing
Image reconstruction
Imaging
Lung
Mathematical models
Medical imaging
model selection
Monitoring
neonatal chest EIT
Neonates
Noise measurement
Parameters
patient-specific prior model
Patients
Pediatrics
Statistical analysis
thorax modelling
Tomography
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Summary: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.
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2021.3053463