Contrast between not fully mineralized tissues is weak and limits conventional computed tomography (CT). An automated grayscale histogram-based analysis features could improve the sensitivity to tissue alterations during early bone healing.
Tissue formation in a rat osteotomy model was analyzed using in vivo micro-CT and classified histologically (mineralized, cartilage, and connective tissues). A conventional threshold-based method including manual contouring was compared to a novel moment-based method: after removing the background peak, the histograms of each slice were characterized by their moments and analyzed as a function of the position along the long bone axis.
The threshold-based method could differentiate between the mineralized and connective tissue (R 2 = 0.73). The moment-based approach yielded a clear distinction between all 3 groups with a classification accuracy up to R 2 = 0.93.
The moment-based evaluation outperforms the conventional threshold-based CT analysis in sensitivity to the healing stage, user independence, and time consumption.
From the *Julius Wolff Institute and Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Berlin, Germany, and †Berlin-Brandenburg School for Regenerative Therapies, Charité–Universitätsmedizin Berlin, Berlin, Germany; ‡European Synchrotron Radiation Facility, Grenoble, France; and §CNRS UMR 5220, INSERM U1044, Université de Lyon, INSA Lyon, Villeurbanne Cedex, France.
Received for publication April 5, 2012; accepted May 9, 2012.
Reprints: Kay Raum, PhD, Julius Wolff Institute and Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany (e-mail: email@example.com).
This work has been supported by the Deutsche Forschungsgemeinschaft in the framework of the Berlin-Brandenburg School for Regenerative Therapies GSC 203, SFB 760 (project G-2.2), and grant Ra1380/8-1.
Bernd Preininger and Bernhard Hesse contributed equally to this work.
The authors report no conflicts of interest.