Load-dependent NMR low-field profiling and relaxation dispersion study of osteoarthritic articular cartilage

At low magnetic fields, T1 variation within cartilage represents a robust parameter that is employed to quantify the layered structure in the tissue and is sensitive to factors such as enzymatic degradation, external load, and degeneration such as osteoarthritis. Variable-field relaxometry, on the other hand, provides access to the quadrupolar dips, i.e. enhanced relaxation rates of 1H particularly at field strengths between 50 and 70 mT, that probe proton-nitrogen interaction and thus the content and local order of macromolecular constituents, namely glycosaminoglycans and collagen. At the same time, an strong overall dispersion of T1 is observed over the whole accessible range of magnetic fields upward from 0.25 mT.

In this study on 20 human cartilage samples, low-field and variable-field techniques were combined for the first time to correlate corresponding NMR parameters and the response to load with the severity of osteoarthritis. The magnitude of the quadrupolar dips, as well as cartilage thickness obtained from profile measurements, is found to correlate with the severity of osteoarthritis. At the same time, a significant correlation was identified for relaxation time variation before and after uniaxial compression at 0.6 MPa, a typical value for forces appearing in the human knee and hip joint. This finding is of importance since the spatial resolution of 50 μm obtained with the single-sided scanner is about one order of magnitude better than the one in clinical high-field or low-field scanners, thus allowing a much more detailed investigation and yet providing constraints for the interpretation of averaged values obtained with whole-body scanners.

Rößler, E., Mattea, C., Stapf, S., Karhula, S., Saarakkala, S., & Nieminen, M. T. (2018). Load-dependent NMR low-field profiling and relaxation dispersion study of osteoarthritic articular cartilage. Microporous and Mesoporous Materials, 269, 160–165. (open source) https://doi.org/10.1016/j.micromeso.2017.02.069