A new method to determine cortical bone thickness in CT images using a hybrid approach of parametric profile representation and local adaptive thresholds: Accuracy results - Museyko, Oleg; Gerner, Bastian; Engelke, Klaus (PLoS ONE, 2017)
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.
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Automated three-dimensional registration of high-resolution peripheral quantitative computed tomography data to quantify size and shape changes of arthritic bone erosions - Toepfer, Dominique; Gerner, Bastian; Finzel, Stephanie; Kraus, Sebastian; Museyko, Oleg; Schett, Georg; Engelke, Klaus (Rheumatology, 2015)
To monitor size and shape changes of bone erosions and changes in BMD in the vicinity of the erosion and in the periarticular trabecular compartment of patients with RA using high-resolution peripheral quantitative CT (HR-pQCT) imaging and to compare an automated three-dimensional (3D) image processing technique with manual measurements of erosion width and depth.The shape of 40 bone erosions and composition of bone around the erosions were analysed in the MCP joints of 22 RA patients both manually and by semi-automated 3D image processing at two different time points. Periosteal segmentation was performed using volume growing and morphological operations. Image registration was applied for transfer of baseline segmentations to follow-up datasets.Eight erosions decreased in size, 6 increased and 28 remained stable. Increasing erosions were more spherical and smaller at baseline compared with decreasing or stable erosions. BMD in the vicinity of shrinking erosions increased, while it decreased next to expanding erosions. There was moderate agreement in the determination of erosion volume between semi-automated and manual measurements, but agreement was poor when assessing changes in volume over time.Longitudinal changes in erosion size and shape and of BMD in the vicinity of an erosion can be measured. BMD changes are associated with progression and regression of erosions. However, the semi-automated and manual approaches did not classify longitudinal changes of erosion volume in the same way. Further research is necessary to define the nature of these differences.
EVALuna2:10105