Ultrasound transmission technique as a potential tool for physical evaluation of monolithic matrix tablets.

The aim of this study was to investigate the effects of tablet porosity and particle size fraction of compacted Starch acetate powders, with and without model drug caffeine, on acoustic properties of tablets. The ultrasound velocity was determined from the transmission measurements. Tablets of starch acetate (SA DS 2.7) powder with two particle size fractions of 0-53 and 0-710 microm were compressed with a compaction simulator. Porosities of tablets varied in the range from 12% to 43% for both particle size fractions. Strong associations were found between the ultrasound velocity and physical properties of the tablets such as porosity and particle size fraction. Interestingly, ultrasound velocity was practically insensitive to inclusion of the model drug caffeine with the concentrations used. Based on this study ultrasound transmission method is a potential non-destructive tool for studying structural changes of tablets and other solid dosage forms.

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.

Multiresolution local tomography in dental radiology using wavelets.

A Bayesian multiresolution model for local tomography in dental radiology is proposed. In this model a wavelet basis is used to present dental structures and the prior information is modeled in terms of Besov norm penalty. The proposed wavelet-based multiresolution method is used to reduce the number of unknowns in the reconstruction problem by abandoning fine-scale wavelets outside the region of interest (ROI). This multiresolution model allows significant reduction in the number of unknowns without the loss of reconstruction accuracy inside the ROI. The feasibility of the proposed method is tested with two-dimensional (2D) examples using simulated and experimental projection data from dental specimens.