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Proceedings Paper

Bayesian multiresolution method for local X-ray tomography in dental radiology
Author(s): Kati Niinimäki; Samuli Siltanen; Ville Kolehmainen
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Paper Abstract

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 Bayesian multiresolution method is proposed for the local tomography reconstruction. The inverse problem is formulated in a well-posed statistical form where a prior model of the tissues compensates for the incomplete projection data. Tissues are represented in a reduced wavelet basis, and prior information is modeled in terms of a Besov norm penalty. The number of unknowns in the inverse problem is reduced by abandoning fine-scale wavelets outside the ROI. Compared to traditional voxel based reconstruction methods, this multiresolution approach allows significant reduction in number of unknown parameters without loss of reconstruction accuracy inside the ROI, as shown by two dimensional examples using simulated local tomography data.

Paper Details

Date Published: 2 February 2009
PDF: 11 pages
Proc. SPIE 7246, Computational Imaging VII, 72460A (2 February 2009); doi: 10.1117/12.815276
Show Author Affiliations
Kati Niinimäki, Univ. of Kuopio (Finland)
Samuli Siltanen, Tampere Univ. of Technology (Finland)
Ville Kolehmainen, Univ. of Kuopio (Finland)


Published in SPIE Proceedings Vol. 7246:
Computational Imaging VII
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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