In clinical CT images containing thin osseous structures, accurate definition of the geometry and density is limited by the scanner's resolution and radiation dose. This study presents and validates a practical methodology for restoring information about thin bone structure by volumetric deblurring of images. The methodology involves 2 steps: a phantom-free, post-reconstruction estimation of the 3D point spread function (PSF) from CT data sets, followed by iterative deconvolution using the PSF estimate. Performance of 5 iterative deconvolution algorithms, blind, Richardson-Lucy (standard, plus Total Variation versions), modified residual norm steepest descent (MRNSD), and Conjugate Gradient Least-Squares were evaluated using CT scans of synthetic cortical bone phantoms. The MRNSD algorithm resulted in the highest relative deblurring performance as assessed by a cortical bone thickness error (0.18 mm) and intensity error (150 HU), and was subsequently applied on a CT image of a cadaveric skull. Performance was compared against micro-CT images of the excised thin cortical bone samples from the skull (average thickness 1.08 ± 0.77 mm). Error in quantitative measurements made from the deblurred images was reduced 82% (p < 0.01) for cortical thickness and 55% (p < 0.01) for bone mineral mass. These results demonstrate a significant restoration of geometrical and radiological density information derived for thin osseous features