title: Application and evaluation of a measured spatially variant system model for PET image reconstruction.
abstract: Accurate system modeling in tomographic image reconstruction has been shown to reduce the spatial variance of resolution and improve quantitative accuracy. System modeling can be improved through analytic calculations, Monte Carlo simulations, and physical measurements. The purpose of this work is to improve clinical fully-3-D reconstruction without substantially increasing computation time. We present a practical method for measuring the detector blurring component of a whole-body positron emission tomography (PET) system to form an approximate system model for use with fully-3-D reconstruction. We employ Monte Carlo simulations to show that a non-collimated point source is acceptable for modeling the radial blurring present in a PET tomograph and we justify the use of a Na22 point source for collecting these measurements. We measure the system response on a whole-body scanner, simplify it to a 2-D function, and incorporate a parameterized version of this response into a modified fully-3-D OSEM algorithm. Empirical testing of the signal versus noise benefits reveal roughly a 15% improvement in spatial resolution and 10% improvement in contrast at matched image noise levels. Convergence analysis demonstrates improved resolution and contrast versus noise properties can be achieved with the proposed method with similar computation time as the conventional approach. Comparison of the measured spatially variant and invariant reconstruction revealed similar performance with conventional image metrics. Edge artifacts, which are a common artifact of resolution-modeled reconstruction methods, were less apparent in the spatially variant method than in the invariant method. With the proposed and other resolution-modeled reconstruction methods, edge artifacts need to be studied in more detail to determine the optimal tradeoff of resolution/contrast enhancement and edge fidelity.
authors.affiliation: Department of Radiology, University of Washington Medical Center, Seattle, WA 98195, USA. aalessio@u.washington.edu
authors.forename: Adam M
authors.initials: AM
authors.lastname: Alessio
authors.forename: Charles W
authors.initials: CW
authors.lastname: Stearns
authors.forename: Shan
authors.initials: S
authors.lastname: Tong
authors.forename: Steven G
authors.initials: SG
authors.lastname: Ross
authors.forename: Steve
authors.initials: S
authors.lastname: Kohlmyer
authors.forename: Alex
authors.initials: A
authors.lastname: Ganin
authors.forename: Paul E
authors.initials: PE
authors.lastname: Kinahan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D003287:Contrast Media; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009010:Monte Carlo Method; D016011:Normal Distribution; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D051598:Whole Body Imaging
chemical_list: D003287:Contrast Media