Whole knee joint MR image datasets were used to compare the performance of geometric trabecular bone features and advanced machine learning techniques in predicting biomechanical strength propertiesmeasured on the corresponding ex vivo specimens. Changes of trabecular bone structure throughout the proximal tibia are indicative of several musculoskeletal disorders involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging also allows non-invasive 3-D characterization of bone microstructure. Sophisticated features like the scaling index method (SIM) can estimate local structural and geometric properties of the trabecular bone and may improve the ability of MR imaging to determine local bone quality in vivo. A set of 67 bone cubes was extracted from knee specimens and their biomechanical strength estimated by the yield stress (YS) [inMPa] was determined throughmechanical testing. The regional apparent bone volume fraction (BVF) and SIM derived features were calculated for each bone cube. A linear multiregression analysis (MultiReg) and a optimized support vector regression (SVR) algorithm were used to predict the YS from the image features. The prediction accuracy was measured by the root mean square error (RMSE) for each image feature on independent test sets. The best prediction result with the lowest prediction error of RMSE = 1.021 MPa was obtained with a combination of BVF and SIM features and by using SVR. The prediction accuracy with only SIM features and SVR (RMSE= 1.023 MPa) was still significantly better than BVF alone and MultiReg (RMSE=1.073MPa). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR-based determination of trabecular bone quality.
Keywords: Biomechanical properties; geometric features; MR imaging; supervised learning; trabecular bone