Biophysics and Biochemistry of Cartilage by NMR and MRI
CHAPTER 18: Multicomponent Relaxation in NMR and MRI of Cartilage
Published:09 Nov 2016
Special Collection: 2016 ebook collectionSeries: New Developments in NMR
D. A. Reiter, R. G. Spencer, and Y. Xia, in Biophysics and Biochemistry of Cartilage by NMR and MRI, ed. Y. Xia and K. Momot, The Royal Society of Chemistry, 2016, pp. 471-493.
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In nuclear magnetic resonance (NMR) and magnetic resonance imaging measurement of relaxation times, deviation from mono-exponential relaxation decay has been shown in a variety of biological tissues and solutions of macromolecules. In these systems, relaxation process can be better described by multiple exponentials, each representing a more-or-less distinct water component in the sample, with a particular fraction size and relaxation time. This approach immediately establishes a much more direct relationship between the relaxation signal and underlying matrix properties than is demonstrated by relaxation times. In articular cartilage, the rapidly, intermediately, and slowly relaxing water components could be associated with collagen, proteoglycans, and bulk water, respectively. The ability to accurately measure and reliably interpret multicomponent T2 and T1ρ relaxation in articular cartilage is challenging and influenced by the complexity of the specimen composition, instrumentation, experimental details, and data-analysis methods. Indeed, there is a good deal of inconsistency among the high-field multicomponent relaxometry studies in the literature in terms of the specifics of multicomponent outcomes, particularly in nasal and articular cartilage, in spite of seemingly similar methodology. This chapter examines a number of these issues and their impact on the robustness of multi-exponential relaxation analysis. Much of this work using high-field small-bore NMR instruments has shown promise for improved assessment of cartilage composition using multicomponent analysis. Some newly emerging imaging acquisition methods and signal models could show promise for extension of multicomponent analysis at high field to human clinical application.