Higher Order Total Variation Super-Resolution from a Single Trabecular Bone Image

Abstract : Osteoporosis is characterized by a low bone mass density and deterioration of bone micro-architecture. Despite the considerable progress in Computed Tomography (CT), the investigation of 3D trabecular bone micro-architecture in-vivo remains limited due to a lack of spatial resolution compared to the trabeculae size. To improve the analysis of trabecular bone from in-vivo CT images, we investigate super-resolution methods to estimate a higher spatial resolution image from a single lower spatial resolution image. To solve this inverse problem, we considered two regularization strategies involving first or second order differential operators. The methods are tested on experimental micro-CT trabecular bone images at 20 µm which are used as reference images. The first tests suggest that both methods give similar results but total variation regularization implemented with the alternating direction method of multipliers algorithm is more efficient to recover correctly some structural parameters.