Development and evaluation of ligament phantoms targeted for shear wave tensiometry

A Modular, Mechanical Knee Model for the Development and Validation of Robotic Testing Methodologies

Six-degree-of-freedom robotic testing is used to gain insight into knee function by measuring the biomechanics of cadaveric knees. However, it can be challenging to use cadaveric knees to validate robotic testing methodologies and to compare methodologies across laboratories because cadavers have variable properties and require lengthy preparation. Therefore, our primary objective was to develop a modular, mechanical knee model for robotic testing with comparable biomechanics to those of human cadaveric knees. A secondary objective was to use the knee model to benchmark the errors in ligament tensions measured using the superposition method, which is a common robotic testing workflow to determine in situ ligament tensions. We designed a knee model consisting of femur and tibia components that are constrained by their articular geometries and by ligament phantoms. We used our robotic testing system to measure the kinetic-kinematic relationships under anterior-posterior, varus-valgus, and internal-external rotation loading in four knee models with different design features. We achieved variable kinetic-kinematic relationships across the knee models by tensioning secondary restraints, altering the engagement of the ligament phantoms, and incorporating osteoarthritic features. The knee models had comparable laxities to cadaveric knees, although most knee models did not capture the flexion-dependent kinematics of cadaveric knees. We also found comparable errors in superposition-computed tensions in the lateral collateral ligament between the knee models and cadaveric knees. Therefore, the knee model is a biomechanically representative specimen that can be a valuable tool for developing and validating robotic testing methodologies.

Assessment of Mechanically Induced Changes in Helical Fiber Microstructure Using Diffusion Tensor Imaging

Noninvasive methods to detect microstructural changes in collagen-based fibrous tissues are necessary to differentiate healthy from damaged tissues in vivo but are sparse. Diffusion Tensor Imaging (DTI) is a noninvasive imaging technique used to quantitatively infer tissue microstructure with previous work primarily focused in neuroimaging applications. Yet, it is still unclear how DTI metrics relate to fiber microstructure and function in musculoskeletal tissues such as ligament and tendon, in part because of the high heterogeneity inherent to such tissues. To address this limitation, we assessed the ability of DTI to detect microstructural changes caused by mechanical loading in tissue-mimicking helical fiber constructs of known structure. Using high-resolution optical and micro-computed tomography imaging, we found that static and fatigue loading resulted in decreased sample diameter and a re-alignment of the macro-scale fiber twist angle similar with the direction of loading. However, DTI and micro-computed tomography measurements suggest microstructural differences in the effect of static versus fatigue loading that were not apparent at the bulk level. Specifically, static load resulted in an increase in diffusion anisotropy and a decrease in radial diffusivity suggesting radially uniform fiber compaction. In contrast, fatigue loads resulted in increased diffusivity in all directions and a change in the alignment of the principal diffusion direction away from the constructs' main axis suggesting fiber compaction and microstructural disruptions in fiber architecture. These results provide quantitative evidence of the ability of DTI to detect mechanically induced changes in tissue microstructure that are not apparent at the bulk level, thus confirming its potential as a noninvasive measure of microstructure in helically architected collagen-based tissues, such as ligaments and tendons.