The biomechanics of the knee following injury and reconstruction of the posterior cruciate ligament c Louis DeFrate.

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005."June 2005."Includes bibliographical references (leaves 199-216).Very little is known regarding the function of the posterior cruciate ligament in response to physiological loading conditions. A limited understanding of posterior cruciate ligament function might contribute to the poor clinical outcomes that are observed after reconstruction. Therefore, the objectives of this thesis were to quantify the biomechanical function of the posterior cruciate ligament both in-vitro and in-vivo and to investigate the effects of injury and reconstruction of the posterior cruciate ligament on knee joint biomechanics. First, muscle loading conditions were simulated in cadavers to measure the effects of posterior cruciate ligament injury and reconstruction on knee joint kinematics and contact pressures. Next, the structural properties of the grafts used in posterior cruciate ligament reconstructions were optimized using a theoretical model. In order to verify these results using an experimental model, an imaging system was developed to measure the strain distributions around the graft surface during tensile testing. Finally, the deformation of the posterior cruciate ligament was studied in living subjects using imaging and solid modeling techniques. Three-dimensional models of the knee joint, including the insertion sites of the posterior cruciate ligament were created from magnetic resonance images. The subjects then flexed their knees as they were imaged using fluoroscopy from two orthogonal directions. The models and orthogonal images were imported into a solid modeling software and used to reproduce the kinematics of the knee as a function of flexion. From these models, the three- dimensional deformation of the posterior cruciate ligament insertion sites was measured.(cont.) These data illustrated that the in-vivo function of the posterior cruciate ligament is different from that observed in in-vitro studies. Current surgical treatments of posterior cruciate ligament injuries do not account for the in-vivo function observed in this study. In summary, this thesis quantified the biomechanical role of the posterior cruciate ligament in response to physiological loading conditions. In addition, grafts used to reconstruct the posterior cruciate ligament were optimized. These data provide valuable information for developing surgical treatments that recreate the in-vivo biomechanics of the posterior cruciate ligament.Sc.D

The biomechanical response of the interosseous membrane of the human forearm : an in-vitro investigation using robotics technology

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001."February 2001."Includes bibliographical references (leaves 48-49).by Louis E. DeFrate.S.M

Automated segmentation and prediction of intervertebral disc morphology and uniaxial deformations from MRI

Objective: The measurement of in vivo intervertebral disc (IVD) mechanics may be used to understand the etiology of IVD degeneration and low back pain (LBP). To this end, our lab has developed methods to measure IVD morphology and uniaxial compressive deformation (% change in IVD height) resulting from dynamic activity, in vivo, using magnetic resonance images (MRI). However, due to the time-intensive nature of manual image segmentation, we sought to validate an image segmentation algorithm that could accurately and reliably reproduce models of in vivo tissue mechanics. Design: Therefore, we developed and evaluated two commonly employed deep learning architectures (2D and 3D U-Net) for the segmentation of IVDs from MRI. The performance of these models was evaluated for morphological accuracy by comparing predicted IVD segmentations (Dice similarity coefficient, mDSC; average surface distance, ASD) to manual (ground truth) measures. Likewise, functional reliability and precision were assessed by evaluating the intraclass correlation coefficient (ICC) and standard error of measurement (SEm) of predicted and manually derived deformation measures. Results: Peak model performance was obtained using the 3D U-net architecture, yielding a maximum mDSC ​= ​0.9824 and component-wise ASDx ​= ​0.0683 ​mm; ASDy ​= ​0.0335 ​mm; ASDz ​= ​0.0329 ​mm. Functional model performance demonstrated excellent reliability ICC ​= ​0.926 and precision SEm ​= ​0.42%. Conclusions: This study demonstrated that a deep learning framework can precisely and reliably automate measures of IVD function, drastically improving the throughput of these time-intensive methods