Medical image registration is process for determining the correspondence of features between two medical collected at different times and different position or using different imaging modalities, where the aim is to follow changes in two imaging. The aim in Electrical Impedance Tomography (EIT) is to exploit the difference in the passive electrical properties of tissues in order to generate a tomographic image. The advantage of such a technique over other imaging modalities is that it provides a non-invasive and sensitive method of probing the body using non ionizing radiation. This paper will Shows the reconstruction of image registration and application in multi frequency electrical impedance tomography. Using by the determination of the appropriate coordinate transfer function (CTF), the image reconstruction has been improved, and the all information in the image can be obtained.
Amani Soliman, (3-2018)

: Electrical Impedance Tomography (EIT) is a non invasive imaging technique with widespread applications in medicine and industry. It aims to image the conductivity distribution within an object by making electrical measurements on the surface of the volume. Single frequency electrical impedance tomography offers many positive attributes which are now being exploited clinically, but there are also problems which have been identified. Multi-frequency electrical impedance tomography has been proposed to solve this problem by making impedance measurements over a range of frequencies. The reason for this is that the impedance of biological tissues varies with frequency, and the variations are different from tissue to tissue. It is therefore possible, in principle to make static image (i.e. images of the distribution of frequency dependence of the impedance within the body) by measuring tissue impedance over a range of frequencies. In this paper new method concerning multi-frequency electrical impedance tomography have been developed. In the this new developed method; current density and finite element method have been used for the derivation of the complex sensitivity matrix. The new developed method was applied in some models and acceptable results were found. By using these method it possible to produce improvement and fast image reconstructions.
Amani SOLIMAN(3-2012)