title: DCT-based complexity regularization for EM tomographic reconstruction.
abstract: This paper introduces a simple algorithm for tomographic reconstruction based on the use of a complexity regularization term. The regularization is formulated in the discrete cosine transform (DCT) domain by promoting a low-noise reconstruction having a high sparsity in the frequency domain. The resulting algorithm simply alternates between a maximum-likelihood (ML) expectation-maximization (EM) update and a decreasing sparsity constraint in the DCT domain. Applications to SPECT reconstruction and comparisons with a classical estimator using the best available regularization terms are given in order to illustrate the potential of our reconstruction technique.
authors.affiliation: Département d'Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montréal H3C 3J7, QC, Canada. mignotte@iro.umontreal.ca
authors.forename: Max
authors.initials: M
authors.lastname: Mignotte
authors.forename: Jean
authors.initials: J
authors.lastname: Meunier
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Soucy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*