Compressive sampling in computed tomography: Method and application

Since Donoho and Candes et al. published their groundbreaking work on compressive sampling or compressive sensing (CS), CS theory has attracted a lot of attention and become a hot topic, especially in biomedical imaging. Specifically, some CS based methods have been developed to enable accurate reconstruction from sparse data in computed tomography (CT) imaging. In this paper, we will review the progress in CS based CT from aspects of three fundamental requirements of CS: sparse representation, incoherent sampling and reconstruction algorithm. In addition, some potential applications of compressive sampling in CT are introduced.