Abstract— The paper considers a new iterative algorithm for tomographic image reconstruction when a very limited number of projections are registered. The algorithm uses a priori information on the discrete values of the object function to be reconstructed and is based on the known multiplicative algebraic reconstruction technique (MART). The new algorithm we called MART-AP organizes a cycle of “external” iterations where the image is corrected at each iteration, using the MART and an image mask synthesized with the help of a priori information. It has been shown in numerical experiment that the algorithm proposed helps completely remove streak- like artifacts usually present on the tomograms which were reconstructed in conditions of strongly incomplete data. I. INTRODUCTION HE problem of image reconstruction with strongly incomplete data, for example, when a few (ten or less) projections are only registered, arises in many areas of practical tomography such as industrial nondestructive testing [1], plasma emission tomography [2], tomography of explosion-compressed metal shells [3], tomography of hydrodynamic object [4] etc. Images from few-view computed tomography are usually reconstructed with an algebraic approach based on the derivation of a discrete reconstruction model with which the problem is reduced to the solution of an underdetermined system of algebraic equations [2], [3], [5]. As a rule, the system is inverted with algorithms that optimize its solution with respect to an additional criterion. These are, for example, the multiplicative algebraic reconstruction technique (MART) [6] or the maximum entropy technique (MENT) [7]. But even these techniques are incapable of removing the few- view artifacts if the object has high-contrast low-frequency structures. The artifacts are usually seen as streaks tangent to the boundaries of the structures. Figure 1 demonstrates how the artifacts add noise to the image and does not allow the real structure to be recognized. Figure 1a shows a 2-D test object which is described in section IV, and figure 1b illustrates the result of its MART reconstruction from 9 model projections. The image of figure 1a has only two colors: black and white. And the tomogram of figure 1b is presented as a gray level image. Our numerical experiment assumed sources to be arranged in a 150°-sector at equal angular steps. Manuscript received October 24, 2011. V. V. Vlasov, A. B. Konovalov, and A. S. Uglov are with the Russian Federal Nuclear Center – Zababakhin Institute of Applied Physics, PO Box 245, Snezhinsk Chelyabinsk Region, 456770 Russia (phone: +73514654639; fax: +73514652233; e-mail: a_konov@mail.vega-int.ru). The problem of streak artifact correction is considered in many papers. Most authors come through using either tomogram post-processing [8], or projection pre-processing [9]. Only a few try to modify the reconstruction techniques themselves. So, the authors of [10] proposed an original modification based on the solution of an artifact recognition problem, after which corrections to the cells in the space where artifacts were detected were applied in another way than to the cells in the space which was free of artifacts. Unfortunately, the authors were a success only partly. It becomes possible to improve a tomogram if we know a priori, for example, the discrete values of the object function to be reconstructed [11], [12]. What is of great importance in this case is the way in which we incorporate this information in the algorithm. For example, the threshold segmentation usually used for tomogram post-processing is of low effect. This is demonstrated in figures 1c and 1d. So, what is shown in figure 1c is a result obtained in the segmentation of the tomogram shown in figure 1b, where the threshold value was equal to the halved maximum of intensity, i.e., with no a priori information. And the image shown in figure 1d was obtained in the segmentation with an a priori known threshold. We must admit both unsatisfactory. Until recently An a Priori Information Based Algorithm for Artifact Preventive Reconstruction in Few-View Computed Tomography Vitaly V. Vlasov, Alexander B. Konovalov, and Alexander S. Uglov T (a) (b) (c) (d) Fig. 1. A model test object (a), the result of its reconstruction with MART (b), and the results of tomogram segmentation without (c) and with (d) an a priori known threshold. Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, ISCCSP 2012, Rome, Italy, 2-4 May 2012 978-1-4673-0276-0/12/$31.00 ©2012 IEEE