DOI

10.17077/etd.4ems64ip

Document Type

Thesis

Date of Degree

Spring 2017

Access Restrictions

.

Degree Name

MS (Master of Science)

Degree In

Biomedical Engineering

First Advisor

Jessica E. Goetz

Second Advisor

Nicole M. Grosland

First Committee Member

Dan R Thedens

Second Committee Member

Doug R Pedersen

Abstract

Osteoarthritis is a chronic, deleterious disease of the joints. It currently affects nearly 25 million Americans. Clinically, osteoarthritis presents as joint pain and verified by radiographic evidence of joint space narrowing. Unfortunately, symptomatic osteoarthritis describes the later stages of disease, at which point irreversible cartilage and bone damage has occurred. Cross-sectional imaging modalities offer the promise of visualizing early features of disease, enabling the development and evaluation of interventions to forestall or prevent degenerative change. Modalities of clinical interest include magnetic resonance imaging (MRI) and multi-detector computed tomography (MDCT).

The following work describes the efficacy of MRI-derived measures for the identification and accurate quantification of local and whole joint changes in articular cartilage thickness changes in vivo. This was performed as part of a study investigating the diagnostic potential of clinical morphometric and compositional MRI to identify early features of osteoarthritis in a large animal model of traumatic knee joint injury. Surgically induced trauma consisted of a partial medial meniscectomy and blunt impact of either 0 J, 0.6 J, or 1.2 J to the weight-bearing cartilage of the medial femur. The study was six months in duration. To evaluate the accuracy of MRI-derived measures of cartilage thickness, imaging acquired at time of euthanasia was compared to high-resolution contrast-enhanced micro-computed tomography (micro CT). 3-dimensional multimodal analysis demonstrated that morphometric MRI imaging is sensitive to sub-voxel changes in cartilage thickness. Therefore, MRI is a clinically relevant modality to quantify subtle cartilage damage, thereby presenting an opportunity to identify patients earlier in the disease process.

Public Abstract

Osteoarthritis is a degenerative disease of the joints characterized by progressive loss of joint function, pain and disability. It affects most adults over fifty and is responsible for a significant proportion of direct and indirect costs of musculoskeletal disease. Due to the profound disability of late stage osteoarthritis, it is also associated with a host of comorbidities, including diabetes, heart disease, and obesity.

The hallmark of osteoarthritis is the onset of joint pain. Following a medical exam, a diagnosis of osteoarthritis is commonly confirmed by the presence of joint space narrowing on x-ray images. Unfortunately, diagnostics such as these are only sensitive to the latest stages of osteoarthritis, at which time irreversible damage has been done to the joint tissues, including cartilage and bone. Treatment of symptomatic osteoarthritis is currently limited to pain management and rehabilitation to reduce disability. When non-operative treatments are no longer efficacious, the damaged joint is commonly replaced with an artificial implant. These devices have achieved great success in enabling patients to return to their activities of daily living without debilitating pain.

Nevertheless, preservation of the native joint would be the preferred outcome and has been the primary aim of research efforts in recent years. Efforts have focused on two main areas: identification of the underlying mechanisms of osteoarthritis and development of diagnostics to screen for osteoarthritis in its earliest stages. Advances in either field offer the promise to inform new treatments that will forestall or outright prevent osteoarthritis. This work demonstrates the capacity of currently available clinical imaging to identify degenerative changes in cartilage morphology.

Keywords

biomechanics, imaging, knee, orthopedics, osteoarthritis

Pages

xv, 79 pages

Bibliography

Includes bibliographical references (pages 67-76).

Copyright

Copyright © 2017 David James Heckelsmiller

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