Improving CT Image Analysis of Augmented Bone with Raman Spectroscopy

Abstract

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.