Dental Research Today is a free monthly online journal that collates and summarizes the latest research about Dental, including details on implants, health, hygiene, cosmetic, dentistry.

Bayesian multiresolution method for local tomography in dental x-ray imaging.

Niinimäki K, Siltanen S, Kolehmainen V

Department of Physics, University of Kuopio, PO Box 1627, FIN-70211 Kuopio, Finland. Kati.Niinimaki@uku.fi

Dental tomographic cone-beam x-ray imaging devices record truncated projections and reconstruct a region of interest (ROI) inside the head. Image reconstruction from the resulting local tomography data is an ill-posed inverse problem. A new Bayesian multiresolution method is proposed for local tomography reconstruction. The inverse problem is formulated in a well-posed statistical form where a prior model of the target tissues compensates for the incomplete x-ray projection data. Tissues are represented in a wavelet basis, and prior information is modeled in terms of a Besov norm penalty. The number of unknowns in the reconstruction problem is reduced by abandoning fine-scale wavelets outside the ROI. Compared to traditional voxel-based models, this multiresolution approach allows significant reduction of degrees of freedom without loss of accuracy inside the ROI, as shown by 2D examples using simulated and in vitro local tomography data.

Published 2 November 2007 in Phys Med Biol, 52(22): 6663-78.
Full-text of this article is available online (may require subscription).

© 2004-2008 Dental Research Today. All Rights Reserved.