Investigation of Semi-Coupled Dictionary Learning in 3-D Super Resolution HR-pQCT Imaging

Abstract

High resolution peripheral quantitative CT (HR-pQCT) permits to investigate bone micro-architecture in vivo . While it is a considerable progress over standard CT for this specific application, the spatial resolution remains so far limited for the investigation of bone topology. In this paper, we investigate super resolution techniques to improve 3-D HR-pQCT images via semi-coupled dictionary learning based on the knowledge of high resolution micro-CT images. We propose a method to select the number of atoms and study the impact of patch sizes. To handle the anisotropy of the 3-D bone structure, we propose a 2.5-D strategy learning low and high resolution dictionaries in a semi-coupled way on the three different directions. The results show that this strategy is superior to the application of the method in 2-D. The bone volume fraction is successfully recovered and the estimation of the connectivity is improved in most test samples. The images have a better quality compared with previously studied methods based on total variation (TV) regularization or a combination of TV and a double-well potential. In the future, we expect to further evaluate the method on a larger data set.