Model Selection Based Algorithm in Neonatal Chest EIT

• 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
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3053463

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.