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Motivation: Cortical bone is an important contributor to bone strength and is pivotal to understand the etiology of osteoporotic fractures and the specific mechanisms of antiosteoporotic treatment regimen. 3D computed tomography (CT) can be used to measure cortical thickness, density, and mass in the proximal femur, lumbar vertebrae, and distal forearm. However, the spatial resolution of clinical whole body CT scanners is limited by radiation exposure; partial volume artefacts severely impair the accurate assessment of cortical parameters, in particular in locations where the cortex is thin such as in the lumbar vertebral bodies or in the femoral neck. Method: Model-based deconvolution approaches recover the cortical thickness by numerically deconvolving the image along 1D profiles using an estimated scanner point spread function (PSF) and a hypothesized uniform cortical bone mineral density (reference density). In this work we provide a new essentially analytical unique solution to the model-based cortex recovery problem using few characteristics of the measured profile and thus eliminate the non-linear optimization step for deconvolution. Also, the proposed approach allows to get rid of the PSF in the model and reduces sensitivity to errors in the reference density. Additionally, run-time and memory effective computation of cortical thickness was achieved with the help of a lookup table. Results: The method accuracy and robustness was validated and compared to that of a deconvolution approach recently proposed for cortical bone and of the 50% relative threshold technique: in a simulated environment with noise and various error levels in the reference density and using CT acquisitions of the European Forearm Phantom (EFP II), a modification of a widely used anthropomorphic standard of cortical and trabecular bone compartments that was scanned with various scan protocols. Conclusion: Results of simulations and of phantom data analysis verified the following properties of the new method: 1) Robustness against errors in the reference density. 2) Excellent accuracy on ground truth data with various noise levels. 3) Very fast computation using a lookup table. [ABSTRACT FROM AUTHOR]

Objective. To develop a precise three-dimensional (3D) segmentation technique for bone erosions in high-resolution peripheral quantitative CT (HR-pQCT) datasets to measure their volume, surface area and shape parameters. Assessment of bone erosions in patients with RA is important for diagnosis and evaluation of treatment efficacy. HR-pQCT allows quantifying periarticular bone loss in arthritis.Methods. HR-pQCT scans with a spatial resolution of about 120 µm of the second to fourth metacarpophalangeal joints were acquired in patients with RA. Erosions were identified by placing a seed point in each of them. After applying 3D segmentation, the volume, surface area and sphericity of erosions were calculated. Results were compared with an approximation method using manual measurements. Intra- and interoperator precision analysis was performed for both the 3D segmentation and the manual technique.Results. Forty-three erosions were assessed in 18 datasets. Intra- and interoperator precisions (RMSCV/RMSSD) for erosion volume were 5.66%/0.49 mm3 and 7.76%/0.76 mm3, respectively. The correlation between manual measurements and their simulation using segmentation volumes was r = 0.87. Precision errors for the manual method were 15.39% and 0.36 mm3, respectively.Conclusion. We developed a new precise 3D segmentation technique for quantification of bone erosions in HR-pQCT datasets that correlates to the volume, shape and surface area of the erosion. The technique allows fast and effective measurement of the erosion size and could therefore be helpful for rapid and quantitative assessment of erosion size. [ABSTRACT FROM PUBLISHER]