A registration strategy to characterize DTI-observed changes in skeletal muscle architecture due to passive shortening.

Skeletal muscle architecture is a key determinant of muscle function. Architectural properties such as fascicle length, pennation angle, and curvature can be characterized using Diffusion Tensor Imaging (DTI), but acquiring these data during a contraction is not currently feasible. However, an image registration-based strategy may be able to convert muscle architectural properties observed at rest to their contracted state. As an initial step toward this long-term objective, the aim of this study was to determine if an image registration strategy could be used to convert the whole-muscle average architectural properties observed in the extended joint position to those of a flexed position, following passive rotation. DTI and high-resolution fat/water scans were acquired in the lower leg of seven healthy participants on a 3T MR system in +20° (plantarflexion) and -10° (dorsiflexion) foot positions. The diffusion and anatomical images from the two positions were used to propagate DTI fiber-tracts from seed points along a mesh representation of the aponeurosis of fiber insertion. The -10° and +20° anatomical images were registered and the displacement fields were used to transform the mesh and fiber-tracts from the +20° to the -10° position. Student's paired t-tests were used to compare the mean architectural parameters between the original and transformed fiber-tracts. The whole-muscle average fiber-tract length, pennation angle, curvature, and physiological cross-sectional areas estimates did not differ significantly. DTI fiber-tracts in plantarflexion can be transformed to dorsiflexion position without significantly affecting the average architectural characteristics of the fiber-tracts. In the future, a similar approach could be used to evaluate muscle architecture in a contracted state.

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.

Replication of the tensile behavior of knee ligaments using architected acrylic yarn.

Knee ligament injury diagnosis is achieved by a comparison between the laxity levels sensed by a clinician in the injured and healthy limb. This is a difficult-to-learn task that requires hands-on practice to achieve proficiency. The inclusion of a physical knee simulator with biomechanically realistic passive components such as knee ligaments could provide consistent training for medical students and lead to improved care for knee injury patients. In this study, we developed a material construct that is both adaptable to a physical knee model and capable of replicating the non-linear mechanical behavior of knee ligaments with the use of helically arranged acrylic yarn. The microstructure of four different types of acrylic yarn were measured and then tested under uniaxial tension. While the fiber twist angle was similar amongst the four yarn types (range = 17.9-18.8°), one yarn was distinct with a low ply twist angle (15.2 ± 1.6°) and high packing fraction (Φ=0.32±0.08). These microstructural differences yielded a lower toe length and higher stiffness and best corresponded to ligament mechanical behavior. We then made looped-yarn constructs to modulate the sample's toe length and stiffness. We found that the load-displacement curve of the construct can be tuned by changing the loop length and loop number of the looped-yarn constructs, matching the load-displacement curve of specific knee ligaments. This study shows how spun yarn can be used to replicate the mechanical behavior of knee ligaments, creating synthetic ligament constructs that could enable the construction of biomechanically realistic joints.