MR techniques are sensitive to the early stages of osteoarthritis, characterized by disruption of collagen and loss of proteoglycan (PG), but are of limited specificity. Here, water compartments in normal and trypsin‐degraded bovine nasal cartilage were identified using a nonnegative least squares multiexponential analysis of T₂ relaxation. Three components were detected: T_2,1 = 2.3 ms, T_2,2 = 25.2 ms, and T_2,3 = 96.3 ms, with fractions w₁ = 6.2%, w₂ = 14.5%, and w₃ = 79.3%, respectively. Trypsinization resulted in increased (P < 0.01) values of T_2,2 = 64.2 ms and T_2,3 = 149.4 ms, supporting their assignment to water compartments that are bound and loosely associated with PG, respectively. The T₂ of the rapidly relaxing component was not altered by digestion, supporting assignment to relatively immobile collagen‐bound water. Relaxation data were simulated for a range of TE, number of echoes, and SNR to guide selection of acquisition parameters and assess the accuracy and precision of experimental results. Based on this, the expected experimental accuracy of measured T₂s and associated weights was within 2% and 4% respectively, with precision within 1% and 3%. These results demonstrate the potential of multiexponential T₂ analysis to increase the specificity of MR characterization of cartilage. Magn Reson Med, 2009.

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty‐eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi‐slice T₂‐weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T₂ maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for “progression to symptomatic OA” using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND‐CHRM). WND‐CHRM classified the isolated T₂ maps for the progression to symptomatic OA with 75% accuracy. Clinical significance: Machine learning algorithms applied to T₂ maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243–2250, 2017.

The distribution of cells within tissue-engineered constructs is difficult to study through nondestructive means, such as would be required after implantation. However, cell labeling with iron-containing particles may prove to be a useful approach to this problem, because regions containing such labeled cells have been shown to be readily detectable using magnetic resonance imaging (MRI). In this study, we used the Food and Drug Administration–approved superparamagnetic iron oxide (SPIO) contrast agent Feridex in combination with transfection agents to label chondrocytes and visualize them with MRI in two different tissue-engineered cartilage constructs. Correspondence between labeled cell spatial location as determined using MRI and histology was established. The SPIO-labeling process was found not to affect the phenotype or viability of the chondrocytes or the production of major cartilage matrix constituents. We believe that this method of visualizing and tracking chondrocytes may be useful in the further development of tissue engineered cartilage therapeutics.