Soft tissue degeneration, a leading cause of physical pain and disability, is highly prevalent in articular cartilage and intervertebral discs (IVDs). The interplay of collagen and proteoglycans with interstitial fluid, which provides structured load support, can begin to degrade due to trauma or overuse, often leading debilitating pain. Detection of soft tissue degeneration at its earliest stage is paramount, as the degeneration is an ever-worsening positive feedback loop. Conventional magnetic resonance imaging (MRI), an ideal modality for early detection and prediction of soft tissue degeneration, is limited by a lack of specificity to individual components, an inability to analyze functional changes, and a weak correlation between quantitative MRI (qMRI) metrics and early tissue degeneration.

In this thesis, the enhancement of MRI metric sensitivity to in vivo tissue state is investigated by improving sensitivity of structural variation detection, spatially assessing soft tissue function, and creating a novel biomarker for the analysis of structural and functional imaging data. Chapters 1 and 2 cover the necessary anatomy and physiology and MRI fundamentals for the following chapters. Chapter 3 explores the use of a stretched exponential (SE) model to improve sensitivity to structural variations. The SE form shows promise in distinguishing between healthy intervertebral discs (IVDs) of varying location, improving on conventional MRI metrics. Chapter 4 details the first in vivo study of IVD intratissue strain laying the groundwork for future diseased model work. The functional assessment of soft tissue would allow for the detection of intratissue biomechanical changes that accompany the structural variations in soft tissue degeneration. In Chapters 5 and 6, the use of a new biometric, anatomically relevant spatial gradients, is explored with the aim of improving the correlations between structural and functional qMRI data and tissue health. Utilizing the digital nature of MRI data and finite difference methods, the new biometric shows stronger correlations to disease state and can be applied across various different tissues.

The results presented in this thesis have improved the use of MRI as a tool for modeling, detection, and prediction of soft tissue degeneration.