K Niinimäki et al 2007 Phys. Med. Biol. 52 6663 doi:10.1088/0031-9155/52/22/008
K Niinimäki1, S Siltanen2 and V Kolehmainen1
Show affiliationsDental 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.
Issue 22 (21 November 2007)
Received 13 April 2007, in final form 7 June 2007
Published 26 October 2007
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