ome problems in the field of health or industry require to obtain information from the inside of a body without using invasive methods. Some techniques are able to get qualitative images. However, these images are not enough to solve some problems that require an accurate knowledge. Normally, the tomography processes are used to explore inside of a body. In this particular case, we are using the method called Electrical Impedance Tomography (EIT). The basic operation of this method is as follows: (1) The electrical potential difference is measured in the electrodes placed around the body. This part is known as forward model. (2) Get information from the inside of the body using the measured voltages. This problem is known as inverse problem. There are several approximations to solve this inverse problem. However, these solutions are focused on obtaining qualitative images. In this paper, we show the main challenges of how to obtain quantitative knowledge when Machine Learning techniques are used to solve this inverse problem.
Keywords: Electrical Impedance Tomography, Machine Learning, Artificial Neural Networks, inverse problems