Magnetic Induction Tomography (MIT) is a new and emerging type of tomography technique that is able to map the distribution of all three passive electromagnetic properties, however most of the current interests are focusing on the conductivity and permeability imaging. In an MIT system, coils are used as separate transmitters or sensors, which can generate the background magnetic field and detect the perturbed magnetic field respectively. Through switching technique the same coil can work as transceiver which can generate field at a time and detect the field at another time. Because magnetic field can easily penetrate through the non-conductive barrier, the sensors do not need direct contact with the imaging object. These non-invasive and contactless features make it an attractive technique for many applications compared to the traditional contact electrode based electrical impedance tomography. Recently, MIT has become a promising monitoring technique in industrial process tomography, for example MIT has been used to determine the distribution of liquidised metal and gas (high conductivity two phase flow monitoring) for metal casting applications. In this paper, a low conductivity two phase flow MIT imaging is proposed so the reconstruction of the low contrast samples (< 6 S/m) can be realised, e.g. gas/ionised liquid. An MIT system is developed to test the feasibility. The system utilises 16 coils (8 transmitters and 8 receivers) and an operating frequency of 13 MHz. Three dierent experiments were conducted to evaluate all possible situations of two phase flow imaging: 1) conducting objects inside a non-conducting background, 2) conducting objects inside a conducting ackground (low contrast) and 3) non-conducting objects inside a conducting background. Images are reconstructed using the linearised inverse method with regularisation. An experiment was designed to image the non-conductive samples inside a conducting background, which is used to represent the size varying bubbles in ionised solution. The temporal reconstruction algorithm is used in this dynamic experiment to improve the image accuracy and noise performance.