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In 2001, 6.1 million Australians (32%) had long-term arthritis or a musculoskeletal disorder with almost 1.2 million reporting an associated disability (Bhatia 2005). Osteoarthritis (OA) is the most common form of arthritis affecting nearly 1.4 million Australians and a major cause of chronic pain and disability (Bhatia 2005). This chronic joint disease is characterized by progressive degenerative changes in the anatomical structure of articular cartilage with late stage X-rays showing joint space narrowing, bone sclerosis and osteophytes which eventually result in compromised joint integrity and function leading to significant pain and disability.

Clinically, X-ray examinations do not directly demonstrate cartilage and lack the sensitivity to perform either early diagnosis, as by the time radiographic findings are observed 13% of the cartilage tissue is lost (Jones 2003), or to assess changes in cartilage during short-term studies or drug trials into OA (Graichen 2004, Cicuttini 2005). In contrast, magnetic resonance imaging (MRI) provides excellent visualization of articular cartilage (Figure 1) along with other joint structures and this has generated extensive clinical interest in the development of MR technologies to provide quantitative analyses (Figure 2) of joint structures to facilitate early stage diagnostic a nd management options for OA and other significant joint pathologies.

Figure 1: Sagittal slices through a 3T weDESS MR image of the knee.

Figure 2: Saggital slices of 3T weDESS MRI Knee scan with cartilage segmentation colour coded using cartilage thickness.

For quantitative MRI-based joint analyses to be clinically viable, image processing approaches which generate fast, accurate and reproducible data on the morphometric and biochemical characteristics of joint cartilage, bone and surrounding structures need to be developed for medical specialities such as orthopaedic surgery, sports medicine, and rheumatology. By far the best clinical solution will be a fully automated joint segmentation and quantitative analysis package rather than the expertise- and time-consuming manual or operator dependant approaches used in past research studies for morphological (Ding 2007, Eckstein 2006b, Koo 2005) and biochemical (Li 2007, Glaser 2005) analyses to demonstrate the capacity of MRI for earlier detection of changes and more accurate monitoring of OA disease progression (Glaser 2005, Eckstein 2006ab, Eckstein 2007, Hunter 2009).

Automatic MR Cartilage Analysis

State-of-the art algorithms for segmentation and analysis (Fripp 2007, 2009) are employed to help objectively measure the health and condition of cartilage tissue (Figure 3).

Figure 3: Surface rendering of automatically extracted bone surface and (opaque) cartilage segmentation with colourmap on illustrating extracted cartilage thickness.

Collaboration Partners

This research is supported by an ARC Linkage Grant (LP100200422) and consists of collaboration between the University of Queensland and Siemens Medical.

References

• Bhatia K, Penm E and Rahman N. Arthritis and musculoskeletal conditions in Australia 2005: with a focus on osteoarthritis, rheumatoid arthritis and osteoporosis. ISSN1833 0991; ISBN-13 978 1 74024 507 4;
• G. Jones, C. Ding, F. Scott, M. Glisson, and F. Cicuttini. Early radiographic osteoarthritis is associated with substantial changes in cartilage volume and tibial bone surface area in both males and females. Osteoarthritis and Cartilage 12(2), 169-174 (2003).
• H. Graichen, J. Jakob, R. von Eisenhart-Rothe, K. -H. Englmeier, M. Reiser, F. Eckstein, Validation of cartilage volume and thickness measurements in the human shoulder with quantitative magnetic resonance imaging, Osteoarthritis and Cartilage, 11(7), 2003.
• F. Cicuttini, J. Hankin, G. Jones, and A. Wluka. Comparison of conventional standing knee radiographs and magnetic resonance imaging in assessing progression of tibiofemoral joint osteoarthritis. Osteoarthritis Cartilage 13(8), 722-727 (2005).
• C. Ding, F. Cicuttini, L. Blizzard, F. Scott, and G. Jones. A longitudinal study of the effect of sex and age on rate of change in knee cartilage volume in adults. Rheumatology 46(2), 273-279 (2007).
• F. Eckstein, F. Cicuttini, J.-P. Raynauld, J.C. Waterton, C. Peterfy. Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment, Osteoarthritis and Cartilage, Volume 14, Supplement 1, 2006, Pages 46-75,
• F. Eckstein and D. Burstein and T.M. Link. Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis, NMR Biomed, 19: 822-854, 2006.
• F. Eckstein, M. Kunz, M. Schutzer, M. Hudelmaier, R. Jackson, J. Yu, C. Eaton, and E. Schneider. Two year longitudinal change and testeretest-precision of knee cartilage morphology in a pilot study for the osteoarthritis initiative. Osteoarthritis and Cartilage 15(11), 1326-32 (2007).
• J. Fripp, S. Crozier, S.K. Warfield, and S. Ourselin . Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee, Physics in Medicine and Biology 52(6), volume 52, 1617-1631, March 2007

• J. Fripp, S. Crozier, S.K. Warfield, and S. Ourselin. Automatic Segmentation and Quantitative Analysis of the Articular Cartilages from Magnetic Resonance Images of the Knee. IEEE Transactions on Medical Imaging, 29(1):55-64 , January 2010.

• Glaser et al . New techniques for cartilage imaging: T2 relaxation time and diffusion-weighted MR imaging.Radiol Clin North Am 43(4).
• S. Koo, G. Gold, and T. Andriacchi. Considerations in measuring cartilage thickness using MRI: factors influencing reproducibility and accuracy. Osteoarthritis and Cartilage 13(9), 782-789 (2005).

Last Updated on Monday, 19 December 2011 13:21