Statistical model based analysis of bone mineral density of lumbar spine.
OBJECTIVE
For planning surgical interventions at the spine affected by osteoporosis, accurate information about the local bone quality in terms of anchorage strength for implants is very important. Based on previous work on automated bone quality assessment on the proximal femur with a completely automated model-based approach, this paper describes first applications and results on the lumbar vertebrae.


METHODS
As basis for the analysis, CT datasets of 17 spinal specimens, with a resolution of 0.7 mm x 0.7 mm x 0.7 mm have been used. A combined statistical model of 3D shape and intensity value distribution was created for these datasets and used to predict the measured bone mineral density (BMD). Different regions of interest were tested, model parameters with high correlation with BMD were identified. Leave-one-out tests were performed to evaluate the capability for the BMD-prediction using regression models.


RESULTS
High correlation values (R = 0.94) between measured and predicted BMD were achieved and the high predictive quality of the model could be shown.


CONCLUSIONS
Although the results are only valid for an insufficient small sample size of specimen data, they show a clear potential for clinical application. Therefore, work in the future will focus on clinical validation with larger sample size and the inclusion of biomechanical properties in addition to BMD.
Leber
Stefan
S
Institute for Biomedical Image Analysis, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria. stefan.leber@umit.at
Fritscher
Karl D
KD
Schmoelz
Werner
W
Schubert
Rainer
R
D015502:Absorptiometry, Photon / Q000379:methods* / Q000706:statistics & numerical data; D015519:Bone Density / Q000502:physiology*; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging* / Q000378:metabolism; D015233:Models, Statistical; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*