The purpose of this study was to clarify meniscal displacement and cartilage-meniscus contact behavior in a full extension position and a deep knee flexion position. We also studied whether the meniscal translation pattern correlated with the tibiofemoral cartilage contact kinematics. Magnetic resonance (MR) images were acquired at both positions for 10 subjects using a conventional MR scanner. Subjects achieved a flexion angle averaging 1398 AE 38. Both medial and lateral menisci translated posteriorly on the tibial plateau during deep knee flexion. The posterior translation of the lateral meniscus (8.2 AE 3.2 mm) was greater than the medial (3.3 AE 1.5 mm). This difference was correlated with the difference in tibiofemoral contact kinematics between medial and lateral compartments. Contact areas in deep flexion were approximately 75% those at full extension. In addition, the percentage of area in contact with menisci increased significantly due to deep flexion. Our results related to meniscal translation and tibio-menisco-femoral contact in deep knee flexion, in combination with information about force and pressure in the knee, may lead to a better understanding of the mechanism of meniscal degeneration and osteoarthritis associated with prolonged kneeling and squatting. ß

We have further demonstrated that final implant position is successfully guided by these patient-specific guides, with reproducibility of tibial component placement falling within 2 degrees of the intended target. This level of reproducibility suggests a promise for this technology, and it is hoped this level of accuracy will become the benchmark for the next generation of total ankle arthroplasty.

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 properties measured 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) [in MPa] was determined through mechanical 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 = 1.073 MPa). 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.

If the exit-velocities seen during actual competition exceed the calculated maximum exit-velocities for these age groups, then our preliminary data suggest that modifications to the game of baseball that would reduce the actual exit-velocities and serve as an effective means to reduce the potential for serious or catastrophic injury are warranted.