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24 March 2016 Phantom-less bone mineral density (BMD) measurement using dual energy computed tomography-based 3-material decomposition
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Osteoporosis is a degenerative bone disease usually diagnosed at the manifestation of fragility fractures, which severely endanger the health of especially the elderly. To ensure timely therapeutic countermeasures, noninvasive and widely applicable diagnostic methods are required. Currently the primary quantifiable indicator for bone stability, bone mineral density (BMD), is obtained either by DEXA (Dual-energy X-ray absorptiometry) or qCT (quantitative CT). Both have respective advantages and disadvantages, with DEXA being considered as gold standard. For timely diagnosis of osteoporosis, another CT-based method is presented. A Dual Energy CT reconstruction workflow is being developed to evaluate BMD by evaluating lumbar spine (L1-L4) DE-CT images. The workflow is ROI-based and automated for practical use. A dual energy 3-material decomposition algorithm is used to differentiate bone from soft tissue and fat attenuation. The algorithm uses material attenuation coefficients on different beam energy levels. The bone fraction of the three different tissues is used to calculate the amount of hydroxylapatite in the trabecular bone of the corpus vertebrae inside a predefined ROI. Calibrations have been performed to obtain volumetric bone mineral density (vBMD) without having to add a calibration phantom or to use special scan protocols or hardware. Accuracy and precision are dependent on image noise and comparable to qCT images. Clinical indications are in accordance with the DEXA gold standard. The decomposition-based workflow shows bone degradation effects normally not visible on standard CT images which would induce errors in normal qCT results.
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Philipp Hofmann, Martin Sedlmair, Bernhard Krauss, Julian L. Wichmann, Ralf W. Bauer, Thomas G. Flohr, and Andreas H. Mahnken M.D. "Phantom-less bone mineral density (BMD) measurement using dual energy computed tomography-based 3-material decomposition", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853E (24 March 2016);

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