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A 3D Reconstruction Algorithm for Real-time Simultaneous Multi-Source EIT Imaging for Lung Function Monitoring

Tzu-Jen Kao, Bruce Amm, David Isaacson, Jonathan Newell, Gary Saulnier, Jennifer L. Mueller
doi: https://doi.org/10.1101/2020.05.29.124222
Tzu-Jen Kao
1GE Research, Niskayuna NY 12309, USA
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Bruce Amm
1GE Research, Niskayuna NY 12309, USA
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David Isaacson
2Rensselaer Polytechnic Institute, Troy NY 12180, USA
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Jonathan Newell
2Rensselaer Polytechnic Institute, Troy NY 12180, USA
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Gary Saulnier
3University at Albany, SUNY, Albany NY 12222, USA
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Jennifer L. Mueller
4Colorado State University, Fort Collins CO 80523, USA
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  • For correspondence: mueller@math.colostate.edu
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Abstract

Monitoring regional pulmonary ventilation and pulsatile perfusion changes in a 3D region of interest (ROI) of the lung is a promising application for electrical impedance tomography (EIT). This paper describes a 3D analytical reconstruction algorithm that was embedded in a prototype EIT system to enable a real-time image reconstruction at nearly 20 frames per second for monitoring impedance changes in the chest in real-time. The derivation and results of the 3D analytical forward solution and inverse solution and details of the real-time reconstruction algorithm are given. The algorithm and EIT system are validated with simulated data, in-vitro phantoms, and finally shown to be capable of imaging ventilation and pulsatile perfusion in human subjects. The human subject data was obtained using a high-precision, high-speed and simultaneous multiple current source (SMS-EIT) developed by GE Research. Data was collected using four rows of 8 electrodes for a healthy adult male subject and 2 rows of 16 electrodes for six healthy human female subjects, with one row placed above the breasts and a second row placed at the infra-mammary fold. Each of the female subjects performed a breathing maneuver with a volumetric incentive spirometer, and the volume of air inhaled was calculated from the EIT images. Pulsatile perfusion images were computed from this data, and regional lung filling was also analyzed.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • lncs@springer.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 31, 2020.
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A 3D Reconstruction Algorithm for Real-time Simultaneous Multi-Source EIT Imaging for Lung Function Monitoring
Tzu-Jen Kao, Bruce Amm, David Isaacson, Jonathan Newell, Gary Saulnier, Jennifer L. Mueller
bioRxiv 2020.05.29.124222; doi: https://doi.org/10.1101/2020.05.29.124222
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A 3D Reconstruction Algorithm for Real-time Simultaneous Multi-Source EIT Imaging for Lung Function Monitoring
Tzu-Jen Kao, Bruce Amm, David Isaacson, Jonathan Newell, Gary Saulnier, Jennifer L. Mueller
bioRxiv 2020.05.29.124222; doi: https://doi.org/10.1101/2020.05.29.124222

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