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

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.