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

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 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.

journal_name

Phys Med Biol

authors

Niinimäki K,Siltanen S,Kolehmainen V

doi

10.1088/0031-9155/52/22/008

subject

Has Abstract

pub_date

2007-11-21 00:00:00

pages

6663-78

issue

22

eissn

0031-9155

issn

1361-6560

pii

S0031-9155(07)48271-9

journal_volume

52

pub_type

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