Magnetic Induction Tomography (MIT) is a relatively new and emerging type of tomography techniques that is able to map the conductivity distribution of an object. Its 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, and the area of the research interest has moved from 2D to 3D because of the volumetric nature of electromagnetic field. Three dimensional MIT images provide more information on the conductivity distribution, especially in the axial direction. However, it has been reported that the reconstructed 3D images can be distorted when the imaging object is located at a less sensitive region. Although this distortion can be com- pensated by adjusting the regularisation criteria, this is not practical in real life applications as the prior information about the object's location is often unavailable. This paper presents a memory ecient 4D MIT algorithm which can maintain the image quality under the same regularisation circumstances. Instead of solving each set of measurement individually, the 4D algorithm takes advantage of the correlations between the image and its neighboring data frames to reconstruct 4D of conductivity movements. The 4D algorithm improves the image qualities by increasing the temporal resolution. It also overcomes some sensitivity issues of 3D MIT algorithms and can provide a smoother and stabler result. Several experimental results are presented for validating the propose algorithms.