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The ability of Anisotropic Minkowski Functionals (AMFs) to capture localanisotropy while evaluating topological properties of the underlying gray-levelstructures has been previously demonstrated. We evaluate the ability of thisapproach to characterize local structure properties of trabecular bonemicro-architecture in ex vivo proximal femur specimens, as visualized onmulti-detector CT, for purposes of biomechanical bone strength prediction. Tothis end, volumetric AMFs were computed locally for each voxel of volumes ofinterest (VOI) extracted from the femoral head of 146 specimens. The localanisotropy captured by such AMFs was quantified using a fractional anisotropymeasure; the magnitude and direction of anisotropy at every pixel was stored inhistograms that served as a feature vectors that characterized the VOIs. Alinear multi-regression analysis algorithm was used to predict the failure load(FL) from the feature sets; the predicted FL was compared to the true FLdetermined through biomechanical testing. The prediction performance wasmeasured by the root mean square error (RMSE) for each feature set. The bestprediction performance was obtained from the fractional anisotropy histogram ofAMF Euler Characteristic (RMSE = 1.01 +- 0.13), which was significantly betterthan MDCT-derived mean BMD (RMSE = 1.12 +- 0.16, p<0.05). We conclude that suchanisotropic Minkowski Functionals can capture valuable information regardingregional trabecular bone quality and contribute to improved bone strengthprediction, which is important for improving the clinical assessment ofosteoporotic fracture risk.

Using anisotropic 3D Minkowski functionals for trabecular bone characterization and biomechanical strength prediction in proximal femur specimens