Structural prior enhanced compressed sensing for CT reconstruction with incomplete data

CT image reconstruction from incomplete projection data is a challenging problem. Among massive reconstruction methods, iterative reconstruction based on compressed sensing (CS) is a promising one that enables us to accurately recovery signals from highly under-sample data when the signals have a sparse representation, which usually can be done by the constrained l₁ minimization. The total variation (TV) minimization is a commonly used sparsity constraint, which assumes the target image is piece-wise constant. TV based CS algorithm has been successfully applied to solve many computed tomography problems, such as few views and interior reconstruction. In this work, we proposed a novel CS algorithm combined with a prior image to enhance the TV sparsity, namely structural prior enhanced compressed sensing (SPECS). Numerical simulation indicates SPECS is effective and robust for many kinds of incomplete data cases.