Abstract
In view of the great effects of osteoporosis on public health, it would be of great value to be able to measure the three-dimensional structure of trabecular bone in vivo as a means to diagnose and quantify the disease. The aim of this work was to implement a method for quantitative characterisation of trabecular bone structure using clinical CT.
Several previously described parameters have been calculated from volumes acquired with a 64-slice clinical scanner. Using automated region growing, distance transforms and three-dimensional thinning, measures describing the number, thickness and spacing of bone trabeculae was obtained. Fifteen bone biopsies were analysed. The results were evaluated using micro-CT as reference.
For most parameters studied, the absolute values did not agree well with the reference method, but several parameters were closely correlated with the reference method. The shortcomings appear to be due to the low resolution and high noise level. However, the high correlation found between clinical CT and micro-CT measurements suggest that it might be possible to monitor changes in the trabecular structure in vivo.
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References
Cullum, I.D., Ell, P.J., Ryder, J.P.: X-ray dual-photon absorptiometry: a new method for the measurement of bone density. Br. J. Radiol. 62(739), 587–592 (1989)
Parfitt, A.M., Drezner, M.K., Glorieux, F.H., Kanis, J.A., Malluche, H., Meunier, P.J., Ott, S.M., Recker, R.R.: Bone histomorphometry: standardization of nomenclature, symbols, and units. Report of the ASBMR Histomorphometry Nomenclature Committee. J Bone Miner Res 2(6), 595–610 (1987)
Revol-Muller, C., Peyrin, F., Carrillon, Y., Odet, C.: Automated 3D region growing algorithm based on an assessment function. Pattern Recognition Letters 23(1-3), 137–150 (2002)
Revol-Muller, C., Jourlin, M.: A new minimum variance region growing algorithm for image segmentation. Pattern Recognition Letters 18(3), 249–258 (1997)
Xie, W., Thompson, R.P., Perucchio, R.: A topology-preserving parallel 3D thinning algorithm for extracting the curve skeleton. Pattern Recognition 36(7), 1529–1544 (2003)
Borgefors, G.: On digital distance transforms in three dimensions. Computer Vision and Image Understanding 64(3), 368–376 (1996)
Svensson, S., di Baja, G.S.: Using distance transforms to decompose 3D discrete objects. Image Vision Comput 20(8), 529–540 (2002)
Hildebrand, T., Ruegsegger, P.: A new method for the model-independent assessment of thicness in three-dimensional images. Journal of Microscopy 185(1), 67–75 (1997)
Laib, A., Ruegsegger, P.: Comparison of structure extraction methods for in vivo trabecular bone measurements. Comput Med Imaging Graph 23(2), 69–74 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Petersson, J., Brismar, T., Smedby, Ö. (2006). Analysis of Skeletal Microstructure with Clinical Multislice CT. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_108
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DOI: https://doi.org/10.1007/11866763_108
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