title: Generalization of median root prior reconstruction.
abstract: Penalized iterative algorithms for image reconstruction in emission tomography contain conditions on which kind of images are accepted as solutions. The penalty term has commonly been a function of pairwise pixel differences in the activity in a local neighborhood, such that smooth images are favored. Attempts to ensure better edge and detail preservation involve difficult tailoring of parameter values or the penalty function itself. The previously introduced median root prior (MRP) favors locally monotonic images. MRP preserves sharp edges while reducing locally nonmonotonic noise at the same time. Quantitative properties of MRP are good, because differences in the neighboring pixel values are not penalized as such. The median is used as an estimate for a penalty reference, against which the pixel value is compared when setting the penalty. In order to generalize the class of MRP-type of priors, the standard median was replaced by other order statistic operations, the L and finite-impluse-response median hybrid (FMH) filters. They allow for smoother appearance as they apply linear weighting together with robust nonlinear operations. The images reconstructed using the new MRP-L and MRP-FMH priors are visually more conventional. Good quantitative properties of MRP are not significantly altered by the new priors.
authors.affiliation: Institute of Signal Processing, Tampere University of Technology, PO Box 553, FIN-33 101 Tampere, Finland. sakari.alenius@tut.fi
authors.forename: Sakari
authors.initials: S
authors.lastname: Alenius
authors.forename: Ulla
authors.initials: U
authors.lastname: Ruotsalainen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D011786:Quality Control; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013848:Thigh / Q000000981:diagnostic imaging