Quantitative imaging of cartilage and meniscus for detection of early osteoarthritic degeneration
Abstract/Contents
- 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. This dissertation focuses on the development and application of imaging methods with the potential for early osteoarthritic detection in the knee joint. The first part of this dissertation focuses on the utility of a weight-bearing computed tomography system for cartilage compression detection by assessing manual methods of cartilage segmentation and developing an automated method for segmentation of the CT images. 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. The next part of the dissertation focuses on optimizing the ex situ imaging environment for relaxation time analysis and then applying these methods to a study comparing relaxation times and the equilibrium compression modulus of cartilage. Finally, the last study uses an ACL-injured population to investigate changes to cartilage using MRI over 18-months post-surgery. A cluster analysis approach, which looks at regions of elevated T2 relaxation times in cartilage between MRI scan time-points is applied and shows that changes can be detected in the ACL-injured knees just 3-months following surgery. 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Black, Marianne Susan | |
|---|---|---|
| Degree supervisor | Levenston, Marc Elliot | |
| Thesis advisor | Levenston, Marc Elliot | |
| Thesis advisor | Gold, Garry E | |
| Thesis advisor | Hargreaves, Brian Andrew | |
| Degree committee member | Gold, Garry E | |
| Degree committee member | Hargreaves, Brian Andrew | |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Marianne Susan Black. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Marianne Susan Black
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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