A priori information as a key to extend X-ray tomography from incomplete data

V. Vengrinovich, S. Zolotarev

Institute of Applied Physics NAS of Belarus, Akademicheskaya 16 str., Minsk 220072 , Belarus

Abstract

In many applications, like medical and industrial imaging, for various reasons set of projection images is usually incomplete, and the viewing angle of the reconstructed object is not comprehensive. In these cases classical imaging algorithms are useless and the so-called iterative methods for image reconstruction are applied, while the final image is achieved by the method of successive approximations with certain restrictions (a priori information), imposed on the image under reconstruction. The use of a priori functional significantly reduces the amount of the necessary original projecting information. For this kind of tasks iterative regularization algorithms in many cases provide significantly better results than classical algorithms based on the methods of integral transformations. The article describes the basic principles and recent advances in the development of Bayesian iterative method (BIM) and its applications in various fields, mainly in tomography and restoration of functions from incomplete and noisy data.

Keywords: Image reconstruction; image processing; image enhancement; Bayesian method