title: Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction.
abstract: Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space-variant, asymmetric, and object-dependent even for space-invariant imaging systems. We propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. We have developed computationally efficient methods for PET systems with shift-invariant geometric responses. We demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.
authors.affiliation: EECS Department, University of Michigan, Ann Arbor, 48109 USA. stayman@eecs.umich.edu
authors.forename: J W
authors.initials: JW
authors.lastname: Stayman
authors.forename: J A
authors.initials: JA
authors.lastname: Fessler
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D013909:Thorax / Q000000981:diagnostic imaging*; D014055:Tomography, Emission-Computed / Q000592:standards