The use of a priori information in image reconstruction from limited data

An operator framework for image reconstruction from limited line integral data is presented in this paper. From this framework we derive iterative limited data reconstruction algorithms. The framework applies to arbitrary scanning geometries and arbitrary patterns of missing data. All available a priori information is incorporated by constraint operators. We show how a priori knowledge of the density in part of the object improves the quality of the reconstructed image. The technique is demonstrated with real x-ray data collected on a clinical CT scanner.