Development of Imaging-Based Models for Analyzing the Spatiotemporal Function of Intervertebral Discs
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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.
Deformable Image Registration
Low Back Pain
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