Browsing by Author "DeFrate, Louis E"

More than 400,000 anterior cruciate ligament (ACL) injuries occur annually in the United States, 70% of which are non-contact. A severe consequence of ACL injury is the increased risk of early-onset of osteoarthritis (OA). Importantly, the increased risk of OA persists even if the ACL is surgically reconstructed. Thus, due to the long term physical consequences and high financial burden of treatment, injury prevention and improved reconstruction techniques are critical. However, the causes of non-contact ACL injuries remain unclear, which has hindered efforts to develop effective training programs targeted at preventing these injuries. Improved understanding of the knee motions that increase the risk of ACL injury can inform more effective injury prevention strategies. Furthermore, there is presently limited in vivo data to describe the function of ACL under dynamic loading conditions. Understanding how the ACL functions to stabilize the knee joint under physiologic loading conditions can inform design criteria for grafts used in ACL reconstruction. Grafts that more accurately mimic the native function of the ACL may help prevent these severe long term degenerative changes in the knee joint after injury.

To this end, measurements of in vivo ACL function during knee motion are critical to understanding how non-contact ACL injuries occur and the function of the ACL in stabilizing the joint during activities of daily living. Specifically, identifying the knee motions that increase ACL length and strain can elucidate the mechanisms of non-contact ACL injury, as a taut ligament is more likely to fail. Furthermore, measuring ACL elongation patterns during dynamic activity can inform the design criteria for grafts used in reconstructive surgery. To obtain measurements, 3D imaging techniques that can be used to measure dynamic in vivo ACL elongation and strain at high temporal and spatial resolution are needed.

Thus, in this dissertation a method of measuring knee motion and ACL function during dynamic activity in vivo using high-speed biplanar radiography in combination with magnetic resonance (MR) imaging was developed. In this technique, 3D surface models of the knee joint are created from MR images and registered to high-speed biplanar radiographs of knee motion. The use of MR imaging to model the joint allows for visualization of bone and soft tissue anatomy, in particular the attachment site footprints of the ligaments. By registering the bone models to biplanar radiographs using software developed in this dissertation, the relative positions of the bones and associated ligament attachment site footprints at the time of radiographic imaging can be reproduced. Thus, measurements of knee kinematics and ligament function during dynamic activity can be obtained at high spatial and temporal resolution.

We have applied the techniques developed in this dissertation to obtain novel dynamic in vivo measurements of the mechanical function of the knee joint. Specifically, the physiologic elongation and strain behaviors of the ACL during gait and single-legged jumping were measured. Additionally, the dynamic function of the patellar tendon during single legged jumping was measured. The findings of this dissertation have helped to elucidate the knee kinematics that increase ACL injury vulnerability by identifying the dynamic motions that result in elongation and strain in the ACL. Furthermore, the findings of this dissertation have provided critical data to inform design criteria for grafts used in reconstructive surgery such that reconstructive techniques better mimic the physiologic function of the ACL.

The methodologies described in this dissertation can be applied to study the mechanical behavior of other joints such as the spine, and other soft tissues, such as articular cartilage, under various loading conditions. Therefore, these methods may have a significant impact on the field of biomechanics as a whole, and may have applicability to a number of musculoskeletal applications.

Lumbar intervertebral discs (IVD) play a critical role in facilitating the mobility and load-bearing functionality of the spine. Consequently, degeneration of the IVDs has been linked to the development of low back pain (LBP), a leading cause of disability in the world. While the pathomechanisms leading to the development of IVD degeneration and LBP are heterogeneous and often difficult to discern, it is believed that the changes in IVD function (i.e., mechanics, composition, tissue structure) may be closely related to the development of discogenic LBP. Specifically, because the IVD has a limited capacity to repair itself, disruptions to IVD tissue structures and biochemical composition may enable nervous tissue innervation into the IVD, potentiating the development of discogenic LBP. However, because our ability to study these changes in vivo remains limited, it remains unknown whether or not we can leverage the study of IVD function to identify risk factors associated with the development of LBP prior to their transition to a painful state. Accordingly, the overarching goal of this work is to develop non-invasive imaging techniques which may be used to perform spatiotemporal analyses of IVD kinematics and composition in vivo. Building upon prior work in our lab, Specific Aim 1 of this proposed research first seeks to develop a controlled methods to investigate the links between IVD function, composition and LBP by examining the in vivo response of IVDs to controlled dynamic loading in asymptomatic individuals. Using data generated in the prior aim, Specific Aim 2 then seeks to first develop and validate an image-segmentation method which enables precise kinematic analysis of the IVD to be carried out in an automated fashion, in vivo. Subsequently, Specific Aim 2 then seeks expand our current ability to characterize IVD function in response to dynamic activity by developing and validating a novel methodology for evaluating three-dimensional (3D) internal spatiotemporal changes in IVD kinematics using a novel deep-learning-based deformable image registration network. This dissertation is organized as a collection of original research articles which were conducted during my time as a PhD student in the Musculoskeletal Bioengineering Laboratory. The first of these (Chapter 3 - Increasing BMI Increases Lumbar Intervertebral Disc Deformation Following A Treadmill Walking Stress Test) was published in the Journal of Biomechanics (Coppock et al., 2021) in May 2021. The second of these (Chapter 4 - In vivo Intervertebral Disc Mechanical Deformation Following a Treadmill Walking “Stress Test” is Inversely Related to T1rho Relaxation Time) was published in the Osteoarthritis and Cartilage (Coppock et. al, 2022). The third, and fourth manuscripts are currently under review (Chapter 5 - Automated Segmentation and Prediction of Intervertebral Disc Morphology and Uniaxial Deformations from MRI; Chapter 6 - In Vivo Analysis of Intervertebral Disc Mechanics Using a Diffeomorphic Deep-Learning Approach. Chapter 7 - The Effects of a 6-month Weight Loss Intervention on Physical Function and Serum Biomarkers in Older Adults with and without Osteoarthritis - is published in Osteoarthritis and Cartilage, Open.

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Anterior cruciate ligament (ACL) injuries are one of the most common knee injuries. Unfortunately, the prevention programs have not significantly reduced these traumatic injuries, and debilitating repercussions (i.e. pain, meniscal degeneration, post-traumatic osteoarthritis) occur even after reconstructive or rehabilitative treatment. Therefore, the overall objectives of this dissertation are 1) to quantitatively determine the injury mechanism for non-contact ACL injuries using bone bruises present on magnetic resonance imaging, and 2) to assess cartilage recovery time as a potential imaging biomarker of osteoarthritis in ACL injured subjects. In the first aim, two common bone bruise patterns were determined and used to quantitatively determine the injury position of the knee using numerical optimization. These findings showed that landing in extension with minimal valgus, small internal tibial rotations, and large anterior translations may be high risk for ACL ruptures. In the second aim, a deep learning autosegmentation algorithm was developed on healthy normal knee MRIs and was successfully validated and applied on ACL reconstructed (ACLR) subjects. Then, their cartilage strains and recovery times were calculated, which showed that the ACLR subjects had significantly higher strains and a much longer recovery time, suggesting that ACLR subjects’ cartilage are softer and less able to rebound quickly to their baseline thickness. In conclusion, this dissertation accomplished both goals of providing quantitative in vivo information on both preventing non-contact ACL injuries in the future as well as identifying early changes in cartilage mechanical properties noninvasively through cartilage recovery times.

Osteoarthritis (OA) is a common joint disorder, affecting over 27 million Americans. OA is characterized by the degeneration of cartilage tissue, and presents clinically with joint pain, stiffness, and limited range of motion. As such, it is a leading cause of disability in the United States. Current treatment options for OA focus on relieving pain (either pharmacologically or through surgical joint replacement), but do not treat or reverse cartilage degeneration. A main reason for this is that the diagnosis of OA depends on pain and radiographic findings, which are not present until advanced stages of the disease. Development of therapies focused on treating or reversing OA degeneration would therefore be enhanced if OA pathology was detectable at earlier stages of the disease. Because changes in mechanical properties (i.e. the stiffness and permeability) occur in OA cartilage before pain and radiographic features are visible, measurement of cartilage mechanics may be used for earlier assessment of OA degeneration. As cartilage mechanics are traditionally measured in the ex vivo environment, the goal of this dissertation was to develop a noninvasive methodology for measuring cartilage mechanical properties in vivo.

Specifically, the methodology consists of a combination of noninvasive magnetic resonance imaging (MRI) techniques to quantify in vivo cartilage composition and mechanical response, as well as a statistical model predicting cartilage stiffness based on these MRI measurements. Porcine knee joint cartilage was used to develop the statistical model, where stiffness was quantified in the traditional manner using ex vivo mechanical testing. The statistical model was then applied to in vivo data from a cohort of healthy human volunteers, for whom the noninvasive MRI techniques were used to measure the composition and mechanical response of their tibial cartilage. Thus, human tibial cartilage stiffness in vivo was quantified.

Overall, the in vivo estimates of healthy human tibial cartilage stiffness (ranging from 0.39 ± 0.05 MPa to 1.06 ± 0.24 MPa) compare well with ex vivo measurements of human cadaveric tibial cartilage stiffness (ranging from 0.45 ± 0.28 MPa to 0.65 ± 0.25 MPa). This finding supports the validity of the methodology developed in this dissertation. Future work using this in vivo methodology for measuring cartilage mechanical properties has diverse applications regarding cartilage health. For instance, this technique may be used clinically to provide earlier detection of OA pathology, or it may be used in future biomechanics research to evaluate the efficacy of different therapeutic approaches toward ameliorating OA pathology and restoring healthy cartilage mechanics. Therefore, the methodology for measuring cartilage mechanical properties in vivo developed here represents an important contribution to the fields of biomechanics and OA research.

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It is possible to investigate in vivo musculoskeletal biomechanics using multimodal medical imaging techniques; however, the analysis of medical image sets is often time-prohibitive. In this dissertation, I outline various projects that utilize magnetic resonance imaging (MRI) scans acquired before and after exercise to quantify cartilage thickness changes incurred by the loading activity. A better understanding of cartilage mechanics is crucial for prediction and prevention efforts related to osteoarthritis, patellofemoral pain, and other musculoskeletal conditions. While this cartilage "stress test'' protocol has been used in the past to investigate knee, ankle, and spine mechanics, this work expands the methodology to the shoulder and hip joints and further addresses the impact of various exercises on the knee joint in different subject populations. For instance, I outline how patellofemoral cartilage deforms after a series of single-legged hops in anterior cruciate ligament-deficient and intact knees, how body mass index impacts patellofemoral cartilage strain and T1rho relaxation times in the context of walking, how tibial cartilage T1rho relaxation times change over the course of the day due to activities of daily living, and how pushups affect glenohumeral cartilage. I also discuss the development and validation of a semi-automated technique to isolate bones from MRIs, which reduces the time required for manual segmentation by approximately 75% and thus significantly improves research efficiency. As an expansion of the semi-automatic segmentation work, I will cover how I developed a technique to assess the minimum moment of inertia along the femoral neck from clinical computed tomography (CT) scans, with the goal of understanding relative fracture risks between individuals with and without diabetes. Finally, I quantify running-induced changes in knee cartilage thickness and composition (as measured by T1rho relaxation times), as well as changes in hip joint bone-to-bone distances and hip cartilage T1rho relaxation times. Running is a known activity linked to patellofemoral pain, yet the underlying etiology of this condition is unknown. As both knee and hip kinematics have been linked to patellofemoral pain, the goal was to assess how running influences these joints biomechanically and biochemically to better understand why people suffer from patellofemoral pain.

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