An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
This addresses the demanding task of cortical marker assessment in osteoporosis monitoring, but it is incremental as it builds on existing model-based approaches for medical imaging.
The authors tackled the problem of automatically identifying the cortical surface in vertebral QCT scans for osteoporosis diagnosis, proposing a model-based method that accurately identified the real center of cortex ex-vivo using phantoms and cadaveric data, and demonstrated in-vivo applicability with manual comparisons.
Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution μCT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.