Row-action image reconstruction algorithm using ℓp-norm distance to a reference image

This work investigates the problem of image reconstruction from small number of projection views in x-ray computed tomography (CT) imaging. The number of acquired projection views has a large influence on accuracy and stability of the image reconstruction problem. However, measuring the projection data over small number of views leads to a patient dose reduction and/or minimization of imaging time which become a principal target in many clinical applications. The presented work aims to develop a row-action type reconstruction algorithm that include a priori known information extracted from a reference image. The proposed method is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. The main idea is to include a distance function consisting of ℓ_p norm of the reconstructed image into the cost function for image reconstruction. The constrained minimization problem is then transferred to the corresponding non-constrained maximization dual problem using Lagrangian duality. Experimental results indicate that the proposed reconstruction algorithms can effectively reduce the streak artifacts when a simple reference image is used.