Journal of Applied Mathematics
Volume 2013 (2013), Article ID 271459, 10 pages
Improving CT Image Analysis of Augmented Bone with Raman Spectroscopy
1University of Applied Sciences Salzburg, Markt 136a, 5431 Kuchl, Austria
2University of Salzburg, Department of Materials Research and Physics, Hellbrunnerstraße 34, 5020 Salzburg, Austria
3University of Applied Sciences Upper Austria, Stelzhamerstraße 23, 4600 Wels, Austria
4Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria
5Medical University of Vienna, Karl Donath Laboratory for Hard Tissue and Biomaterial Research, Department of Oral Surgery, Sensengasse 2a, 1090 Wien, Austria
6Austrian Cluster for Tissue Regeneration, Donaueschingenstraße 13, 1200 Vienna, Austria
Received 29 March 2013; Accepted 10 May 2013
Academic Editor: Hang Joon Jo
Copyright © 2013 J. Charwat-Pessler et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In recent years, bone graft substitutes have been increasingly used in the medical field, for example, in order to promote new bone formation. Microcomputed tomography (μ-CT) is an image-guided technique used in medicine as well as in materials science, enabling the characterization of biomaterials with high spatial resolution. X-ray-based methods provide density information; however, the question how far conclusions on chemical structures can be inferred from any kind of CT information has not been intensively investigated yet. In the present study, a bone sample consisting of autogenous bone derived cells (ABCs) and bovine bone mineral (BBM) was investigated by μ-CT and Raman spectroscopic imaging, that is, by two nondestructive imaging methods. Thereby, the image data were compared by means of regression analysis and digital image processing methods. It could be found that 51.8% of the variance of gray level intensities, as a result of μ-CT, can be described by different Raman spectra of particular interest for bone composition studies by means of a multiple linear regression. With the better description of μ-CT images by the linear model, a better distinction of different bone components is possible. Therefore, the method shown can be applied to improve CT-image-based bone modeling in the future.