In clinical computed tomography (CT) images, cortical bone features with sub-millimeter (sub-mm) thickness are substantially blurred, such that their thickness is overestimated and their intensity appears underestimated. Therefore, any inquiry of the geometry or the density of such bones based on these images is severely error prone. We present a model-based method for estimating the true thickness and intensity magnitude of cortical and trabecular bone layers at localized regions of complex shell bones down to 0.25 mm. The method also computes the width of the corresponding point spread function. This approach is applicable on any CT image data, and does not rely on any scanner-specific parameter inputs beyond what is inherently available in the images themselves. The method applied on CT intensity profiles of custom phantoms mimicking shell-bones produced average cortical thickness errors of 0.07 ± 0.04 mm versus an average error of 0.47 ± 0.29 mm in the untreated cases (t(55) = 10.92, p ≪ 0.001)). Similarly, the average error of intensity magnitude estimates of the method were 22 ± 2.2 HU versus an error of 445 ± 137 HU in the untreated cases (t(55) = 26.48, p ≪ 0.001)). The method was also used to correct the CT intensity profiles from a cadaveric specimen of the craniofacial skeleton (CFS) in 15 different regions. There was excellent agreement between the corrections and µCT intensity profiles of the same regions used as a 'gold standard' measure. These results set the groundwork towards restoring cortical bone geometry and intensity information in entire image data sets. This information is essential for the generation of finite element models of the CFS that can accurately describe the biomechanical behavior of its complex thin bone structures.
The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the scanner-specific parameters.
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.
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