title: L0 constrained sparse reconstruction for multi-slice helical CT reconstruction.
abstract: In this paper, we present a Bayesian maximum a posteriori method for multi-slice helical CT reconstruction based on an L0-norm prior. It makes use of a very low number of projections. A set of surrogate potential functions is used to successively approximate the L0-norm function while generating the prior and to accelerate the convergence speed. Simulation results show that the proposed method provides high quality reconstructions with highly sparse sampled noise-free projections. In the presence of noise, the reconstruction quality is still significantly better than the reconstructions obtained with L1-norm or L2-norm priors.
authors.affiliation: Laboratory of Image Science and Technology (LIST), South East University, Nanjing, People's Republic of China.
authors.forename: Yining
authors.initials: Y
authors.lastname: Hu
authors.forename: Lizhe
authors.initials: L
authors.lastname: Xie
authors.forename: Limin
authors.initials: L
authors.lastname: Luo
authors.forename: Jean Claude
authors.initials: JC
authors.lastname: Nunes
authors.forename: Christine
authors.initials: C
authors.lastname: Toumoulin
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D012399:Rotation; D036542:Tomography, Spiral Computed / Q000379:methods*