Quantitative Imaging of Cartilage and Meniscus for Detection of Early Osteoarthritic Degeneration

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URL: http://purl.stanford.edu/dn309qn4326

Abstract

Osteoarthritis is a prevalent disease that results in pain and loss of mobility of the joints. Knee osteoarthritis in particular affects an estimated 37 % of the population over 60 years of age. Osteoarthritis of the knee is typically diagnosed with a weight-bearing radiograph following symptomatic knee pain. Unfortunately, by this point osteoarthritis has usually reached an advanced stage. There are no current cures for osteoarthritis, but to allow for development of disease-modifying treatments it is necessary to develop and validate methods that can reliably detect the earliest osteoarthritic changes.

The first part of this dissertation explores the use of an upright weight-bearing computed tomography (CT) system used in conjunction with an intra-articular contrast injection to track cartilage deformation while a person stands for an extended period of time. The rate of cartilage deformation over time has the potential to be an early marker for osteoarthritic change in the knee, however a major challenge in this project is the time it takes to manually segment the 3D CT images. Chapter 3 describes the development of an automated segmentation method to eliminate the manual cartilage segmentation process. The results show that the automated approach is extremely consistent with the manual results, and importantly could allow for completion of similar studies of cartilage deformation much faster, and with a reduction in subjective user-input.

Magnetic Resonance Imaging (MRI) has high potential for detecting early osteoarthritis due to its excellent soft tissue contrast and ability to measure quantitative relaxation times, such as T2 and T1ρ, which are generally considered to be related to collagen and proteoglycan content, respectively. Ideally, by tracking changes in T2 and T1ρ, the degenerative state of cartilage and meniscus could be determined. Ex situ imaging is often necessary to correlate quantitative MRI metrics with mechanical and biochemical properties of tissue to fully understand what the imaging metrics mean. Unfortunately, in the literature there is a wide variety of approaches for imaging tissue ex situ, which may adversely affect the quantitative metrics. Chapter 4 of this dissertation investigates the use of 5 different substances for immersing cartilage and meniscus tissue in during ex situ imaging, and if they affect quantitative MRI relaxation times relative to in situ and in vivo results. The results of this study show the benefits of using peanut oil for ex situ imaging of cartilage and meniscus tissue for T2 relaxation times in particular. Chapter 5 uses the results of this work in an attempt to correlate T2 relaxation times of cartilage retrieved from knee joint replacement subjects with the equilibrium compressive modulus and water content. Even with advanced imaging methods for T2 relaxation times and the improved ex situ environment, correlations were not clear between T2 relaxation times and the equilibrium compressive modulus or water content of cartilage.

Although T2 relaxation times have been shown to generally increase with osteoarthritis, the variation in T2 relaxation times in all states of cartilage health makes it challenging to use the absolute value as a metric for cartilage health. In Chapter 6, an ACL-injured population is used to investigate changes to cartilage using MRI over 18-months postsurgery. ACL-injured subjects are ideal for studying osteoarthritis due to their substantially increased risk of osteoarthritis and known point of traumatic injury. A cluster analysis approach, which looks at regions of elevated T2 relaxation times in cartilage between MRI scan time-points showed that ACL-injured subjects exhibited significantly higher regions of T2 clusters compared to a control group just 3-months following their baseline MRI scan, and these changes persisted at 18-months post-baseline. The results from this study suggest that the T2 cluster analysis might be ideal for detecting cartilage changes, and potentially more sensitive for detecting cartilage degeneration than relying on absolute values of T2 relaxation times.

Overall, the work from this dissertation progresses the application of imaging for understanding early osteoarthritis by focusing on both CT, MRI and the development of advanced imaging analysis techniques.

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