scholarly journals Electrical Impedance Tomography might be a Practical Tool to Provide Information about COVID-19 Pneumonia Progression

2021 ◽  
Vol 7 (2) ◽  
pp. 276-278
Author(s):  
Rongqing Chen ◽  
András Lovas ◽  
Balázs Benyó ◽  
Knut Moeller

Abstract COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

2018 ◽  
Vol 30 (3) ◽  
pp. 481-504 ◽  
Author(s):  
HABIB AMMARI ◽  
FAOUZI TRIKI ◽  
CHUN-HSIANG TSOU

The multifrequency electrical impedance tomography consists in retrieving the conductivity distribution of a sample by injecting a finite number of currents with multiple frequencies. In this paper, we consider the case where the conductivity distribution is piecewise constant, takes a constant value outside a single smooth anomaly, and a frequency dependent function inside the anomaly itself. Using an original spectral decomposition of the solution of the forward conductivity problem in terms of Poincaré variational eigenelements, we retrieve the Cauchy data corresponding to the extreme case of a perfect conductor, and the conductivity profile. We then reconstruct the anomaly from the Cauchy data. The numerical experiments are conducted using gradient descent optimization algorithms.


2008 ◽  
Vol 84 ◽  
pp. S93
Author(s):  
Elias Chatziioannidis ◽  
Theodoros Samaras ◽  
Anastasios Siountas ◽  
George Mitsiakos ◽  
Nikolaos Nikolaidis

Sign in / Sign up

Export Citation Format

Share Document