Image Reconstruction for COVID-19 Using Multifrequency Electrical Impedance Tomography
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
; : 359-405, 2021.
Article
in English
| Scopus | ID: covidwho-2322199
ABSTRACT
Multifrequency electrical impedance tomography (MfEIT) is a technique that allows the visualization of images inside the body through the characterization of electrical impedance, conductivity or permissiveness in a given frequency range, as well as the characterization of body tissue analyzed. Usually, several alternating electrical currents are injected through electrodes connected to the surface of the body under study, and the resulting voltages are measured and stored for processing and obtaining an image. The image reconstruction algorithm uses the data set of measurements of applied currents and voltages measured at each electrode, calculating the distributions of conductivity, permittivity, or resistivity within the conductive volume studied. The reconstruction of images by direct methods is widely used in applications that require rapid reconstruction and lower computational cost, such as the monitoring of pulmonary mechanical ventilation in ICU beds in patients intubated due to COVID-19. In this chapter, we present the basic characteristics so that a wireless, low-cost, and portable MfEIT system can be implemented, as well as the definitions and modeling of the two-dimensional D-bar method for image reconstruction. Clinical parameters of patients diagnosed with COVID-19 are used to implement some reconstructions of images, as well as to bring a discussion about the efficiency of this technology for this clinical condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
Year:
2021
Document Type:
Article
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