title: Multiresolution local tomography in dental radiology using wavelets.
abstract: A Bayesian multiresolution model for local tomography in dental radiology is proposed. In this model a wavelet basis is used to present dental structures and the prior information is modeled in terms of Besov norm penalty. The proposed wavelet-based multiresolution method is used to reduce the number of unknowns in the reconstruction problem by abandoning fine-scale wavelets outside the region of interest (ROI). This multiresolution model allows significant reduction in the number of unknowns without the loss of reconstruction accuracy inside the ROI. The feasibility of the proposed method is tested with two-dimensional (2D) examples using simulated and experimental projection data from dental specimens.
authors.affiliation: Department of Physics, University of Kuopio, PO Box 1627, FIN-70211 Kuopio, Finland. kati.niinimaki@uku.fi
authors.forename: K
authors.initials: K
authors.lastname: Niinimäki
authors.forename: S
authors.initials: S
authors.lastname: Siltanen
authors.forename: V
authors.initials: V
authors.lastname: Kolehmainen
mesh_terms: D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D011861:Radiography, Dental / Q000379:methods*