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Deep learning based reconstruction enables high-resolution electrical impedance tomography for lung function assessment
Date Issued
2023
Publisher
IEEE
ISBN
9798350324471
9798350324488
ISSN
2694-0604
2375-7477
Citation
Zeng, S., Kwok, W. C., Cao, P., Zouari, F., Lee, P. T. Y., Chan, R. W., & Touboul, A. (2023). Deep learning based reconstruction enables high-resolution electrical impedance tomography for lung function assessment. In IEEE (Ed.) Proceedings of 2023 45th annual international conference of the IEEE engineering in medicine & biology society (EMBC). 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia (pp. 1-4). IEEE.
Type
Conference Paper
Abstract
Recently, deep learning based methods have shown potential as alternative approaches for lung time difference electrical impedance tomography (tdEIT) reconstruction other than traditional regularized least square methods, that have inherent severe ill-posedness and low spatial resolution posing challenges for further interpretation. However, the validation of deep learning reconstruction quality is mainly focused on simulated data rather than in vivo human chest data, and on image quality rather than clinical indicator accuracy. In this study, a variational autoencoder is trained on high-resolution human chest simulations, and inference results on an EIT dataset collected from 22 healthy subjects performing various breathing paradigms are benchmarked with simultaneous spirometry measurements. The deep learning reconstructed global conductivity is significantly correlated with measured volume-time curves with correlation > 0.9. EIT lung function indicators from the reconstruction are also highly correlated with standard spirometry indicators with correlation > 0.75.Clinical Relevance— Our deep learning reconstruction method of lung tdEIT can predict lung volume and spirometry indicators while generating high-resolution EIT images, revealing potential of being a competitive approach in clinical settings.
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