Low-dose X-ray CT reconstruction based on joint sinogram smoothing and learned dictionary-based representation
In this paper we propose two novel image reconstruction methods for low-dose X-ray CT data. Both approaches are based on anisotropic sinogram smoothing coupled with sparse local image representation with respect to a learned over complete dictionary. The redundant dictionary is learned from normal-dose CT training images and encodes artifact-free image behavior. The methods differ in the details of how the redundant dictionary information is included. Efficient solution approaches to the new formulations are provided. Comparative results on simulated low-dose imagery are given. Our approach is new in how it applies learning-based dictionary techniques to low-dose CT reconstruction, in its use of high quality training data in dictionary generation, and in its incorporation of anisotropic sinogram constraints together with the dictionary-based representation.