In vivo evaluation of human patellar tendon microstructure and microcirculation with diffusion MRI

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.

Quantative MRI predicts tendon mechanical behavior, collagen composition, and organization

Quantitative magnetic resonance imaging (qMRI) measures have provided insights into the composition, quality, and structure-function of musculoskeletal tissues. Low signal-to-noise ratio has limited application to tendon. Advances in scanning sequences and sample positioning have improved signal from tendon allowing for evaluation of structure and function. The purpose of this study was to elucidate relationships between tendon qMRI metrics (T1, T2, T1ρ and diffusion tensor imaging [DTI] metrics) with tendon tissue mechanics, collagen concentration and organization. Sixteen human Achilles tendon specimens were collected, imaged with qMRI, and subjected to mechanical testing with quantitative polarized light imaging. T2 values were related to tendon mechanics [peak stress (rsp  = 0.51, p = 0.044), equilibrium stress (rsp  = 0.54, p = 0.033), percent relaxation (rsp  = -0.55, p = 0.027), hysteresis (rsp  = -0.64, p = 0.007), linear modulus (rsp  = 0.67, p = 0.009)]. T1ρ had a statistically significant relationship with percent relaxation (r = 0.50, p = 0.048). Collagen content was significantly related to DTI measures (range of r = 0.56-0.62). T2 values from a single slice of the midportion of human Achilles tendons were strongest predictors of tendon tensile mechanical metrics. DTI diffusivity indices (mean diffusivity, axial diffusivity, radial diffusivity) were strongly correlated with collagen content. These findings build on a growing body of literature supporting the feasibility of qMRI to characterize tendon tissue and noninvasively measure tendon structure and function. Statement of Clinical Significance: Quantitative MRI can be applied to characterize tendon tissue and is a noninvasive measure that relates to tendon composition and mechanical behavior.

Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.