This article discusses the impedance method in the forward calculation in magnetic induction tomography (MIT). Magnetic field and eddy current distributions were obtained numerically for a sphere in the field of a coil and were compared with an analytical model. Additionally, numerical and experimental results for phase sensitivity in MIT were obtained and compared for a cylindrical object in a planar array of sensors. The results showed that the impedance method provides results that agree very well with reality in the frequency range from 100 kHz to 20 MHz and for low conductivity objects (10 S/m or less). This opens the possibility of using this numerical approach in image reconstruction in MIT.

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Brazil is one of the countries most affected by the COVID-19 pandemic. Since the beginning of November 2020, Brazil has been experiencing an acute crisis of the disease, with an increase in cases, hospitalizations and deaths, including among the youngest. During the month of April 2021, as intensive care units they were working almost at full capacity throughout the country. Since the beginning of the pandemic, in March 2020, without total, Brazil has reported more than 14 million cases of COVID- 19 and more than 400 thousand deaths. Due to the rapid spread of the virus and due to the fact that the health systems of different countries are not prepared to serve the large number of patients affected by this disease, we have proposed the use of multifrequency electrical impedance tomography (MfEIT) in the management of pulmonary disease in ICU beds. There are several other forms of tomographic imaging that deliver better image resolution, however, MfEIT has some advantages over CT Scan and X-rays, which are: the absence of ionizing radiation, the portability of the equipment, the possibility of access remote control of the patient's clinical data by the medical team, the visualization of dynamic pulmonary and cardiac parameters that are not seen in computed tomography images, nor in ultrasound images. However, an application of the D-Bar algorithms developed by Siltanen and his team, from 2012 to 2020, at the University of Helsinki, Finland, for viewing images in patients with COVID-19 was evaluated. Various scenarios and criteria were proposed in the text and the results obtained promising evidence for imaging internal organs in the radio frequency range. As expected, codes cannot be considered in extremely low frequency situations, as reconstructions are not considered. In the future, we seek to work with deep neural networks to speed up the simulation of images and to compare results.

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