In electrical impedance tomography (EIT) systems, various current sources were designed to deliver constant electrical current towards the electrodes, irrespective of the conductivity of the conductive medium under test. This, however, is not possible to achieve in practice since the water conductivity ranges from 5 S/m for sea water down-to 5.5 x10^-6 and 5 x 10^-3 S/m for pure and drinking water respectively which are substantially low values [1]. Thus, even in case of high water-cut fluid, unless the water is very salty, the usage of such current sources may not be appropriate and their substitution with an electrical capacitance tomography system or a dielectric measurement sensor may be required. Indeed, even if the conductivity of the medium is known and high, the gross conductivity formed between a given pair of electrodes depend heavily on the phase’s distribution pattern between them and can be excessively low, causing high electric current fluctuations. One of the contributions of this paper is to experimentally assess the effect of the electrical current fluctuations on the accuracy of the EIT image reconstruction using three different cutting-edge and most widely current sources designs. To the authors’ best knowledge, this study is the first of its kind as all other prior works assume that the electrical current is constant in the formulation of the EIT forward and inverse problems. The paper also suggests a new cost-effective measurement circuit design that overcomes the fluctuations. It continuously measures the electrical current consumed during every single excitation cycle using a very high-speed analog-to-digital (ADC) converter, interfaced with Cyclone V field program gate array (FPGA), to compensate for the associate voltage readings accordingly during the image reconstruction procedure. The assessment of the system was conducted experimentally, and the results were compared with those provided by the three current sources. The associated results show the higher accuracy of the suggested design, when using Gauss Newton (GN) method, in terms of mean-squared error, which was decreased by 75%, and the image correlation coefficient as well.