A Bone Sample Containing a Bone Graft Substitute Analyzed by Correlating Density Information Obtained by X-ray Micro Tomography with Compositional Information Obtained by Raman Microscopy

Johann Charwat-Pessler, Maurizio Musso, Alexander Petutschnigg, Karl Entacher, Bernhard Plank, Erik Wernersson, Stefan Tangl, Peter Schuller-Götzburg

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The ability of bone graft substitutes to promote new bone formation has been increasingly used in the medical field to repair skeletal defects or to replace missing bone in a broad range of applications in dentistry and orthopedics. A common way to assess such materials is via micro computed tomography (µ-CT), through the density information content provided by the absorption of X-rays. Information on the chemical composition of a material can be obtained via Raman spectroscopy. By investigating a bone sample from miniature pigs containing the bone graft substitute Bio Oss ® , we pursued the target of assessing to what extent the density information gained by µ-CT imaging matches the chemical information content provided by Raman spectroscopic imaging. Raman images and Raman correlation maps of the investigated sample were used in order to generate a Raman based segmented image by means of an agglomerative, hierarchical cluster analysis. The resulting segments, showing chemically related areas, were subsequently compared with the µ-CT image by means of a one-way ANOVA. We found out that to a certain extent typical gray-level values (and the related histograms) in the µ-CT image can be reliably related to specific segments within the image resulting from the cluster analysis.
Original languageEnglish
Pages (from-to)3831-3853
Number of pages23
JournalMaterials Science
Volume8
Issue number7
DOIs
Publication statusPublished - Jun 2015

Keywords

  • computed tomography
  • Raman spectroscopy
  • bone
  • Bio-Oss
  • cluster analysis
  • image segmentation
  • Cluster analysis
  • Bio-Oss®
  • Image segmentation
  • Bone
  • X-ray micro computed tomography

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