Action of Compression and Cations on the Proton and Deuterium Relaxation in Cartilage

Lüsse, S.; Knauss, Robert; Werner, Annett; Gründer, Wilfried; Arnold, Klaus

doi:10.1002/mrm.1910330405

Cited by 65 publications

(72 citation statements)

References 16 publications

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“…For example, the proton relaxation time constants reported here are consistent with those of previous work in which cartilage compression was performed via an osmotic technique prior to MR evaluation at atmospheric pressure (21). The consistency of the present results with those cited indicates that changes in compression-induced proton MR characteristics can be generated by both mechanical or osmotic pressure gradients.…”

Section: Discussionsupporting

confidence: 93%

Sodium and proton MR properties of cartilage during compression

Regatte

Kaufman

Noyszewski

et al. 1999

J. Magn. Reson. Imaging

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Section: Discussionsupporting

confidence: 93%

Sodium and proton MR properties of cartilage during compression

Regatte

Kaufman

Noyszewski

et al. 1999

J. Magn. Reson. Imaging

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“…Thus, it is possible for the sGAG concentration of the w 1 -and w 2 -associated compartments to increase without an equivalent increase in the fractional weights. Similar considerations apply to the fact that T 23 displayed a higher correlation coefficient with sGAG content than did either T 22 or T 21 .…”

mentioning

confidence: 54%

“…Even-numbered echoes were used for analysis, 9 yielding an effective TE of 1.2 ms, precluding detection of rapidly relaxing collagen-bound water. 20,21 Intensities were fit to a three-parameter monoexponential function to obtain conventional T 2 relaxation times; the same data were used for multiexponential analysis as described in the section ''Fitting of multiexponential T 2 relaxation data. ''…”

Section: Mr Measurementsmentioning

confidence: 99%

Characterization of Engineered Cartilage Constructs Using MultiexponentialT₂Relaxation Analysis and Support Vector Regression

Irrechukwu

Reiter

Lin

et al. 2012

Tissue Engineering Part C: Methods

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Increased sensitivity in the characterization of cartilage matrix status by magnetic resonance (MR) imaging, through the identification of surrogate markers for tissue quality, would be of great use in the noninvasive evaluation of engineered cartilage. Recent advances in MR evaluation of cartilage include multiexponential and multiparametric analysis, which we now extend to engineered cartilage. We studied constructs which developed from chondrocytes seeded in collagen hydrogels. MR measurements of transverse relaxation times were performed on samples after 1, 2, 3, and 4 weeks of development. Corresponding biochemical measurements of sulfated glycosaminoglycan (sGAG) were also performed. sGAG per wet weight increased from 7.74 -1.34 mg/ mg in week 1 to 21.06 -4.14 mg/mg in week 4. Using multiexponential T 2 analysis, we detected at least three distinct water compartments, with T 2 values and weight fractions of (45 ms, 3%), (200 ms, 4%), and (500 ms, 97%), respectively. These values are consistent with known properties of engineered cartilage and previous studies of native cartilage. Correlations between sGAG and MR measurements were examined using conventional univariate analysis with T 2 data from monoexponential fits with individual multiexponential compartment fractions and sums of these fractions, through multiple linear regression based on linear combinations of fractions, and, finally, with multivariate analysis using the support vector regression (SVR) formalism. The phenomenological relationship between T 2 from monoexponential fitting and sGAG exhibited a correlation coefficient of r 2 = 0.56, comparable to the more physically motivated correlations between individual fractions or sums of fractions and sGAG; the correlation based on the sum of the two proteoglycan-associated fractions was r 2 = 0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r 2 in the range 0.43-0.54. However, correlations using SVR exhibited r 2 values in the range 0.68-0.93. These results indicate that the SVR-based multivariate approach was able to determine tissue sGAG with substantially higher accuracy than conventional monoexponential T 2 measurements or conventional regression modeling based on water fractions. This combined technique, in which the results of multiexponential analysis are examined with multivariate statistical techniques, holds the potential to greatly improve the accuracy of cartilage matrix characterization in engineered constructs using noninvasive MR data.

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“…In all cases, changes in water proton relaxation times were readily attributed to changes in tissue hydration, with little discussion as to the role of different tissue compartments on the respective MRI relaxation times (20,21). Furthermore, the diffusion coefficient of water in cartilage over time periods on the order of 10 msec was found to be more sensitive to overall hydration than to matrix composition (19).…”

Section: Discussionmentioning

confidence: 96%

Response of engineered cartilage tissue to biochemical agents as studied by proton magnetic resonance microscopy

Potter

Butler

Horton

et al. 2000

Arthritis & Rheumatism

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Objective. To test the hypothesis that magnetic resonance imaging (MRI) results correlate with the biochemical composition of cartilage matrix and can therefore be used to evaluate natural tissue development and the effects of biologic interventions.Methods. Chondrocytes harvested from day-16 chick embryo sterna were inoculated into an MRIcompatible hollow-fiber bioreactor. The tissue that formed over a period of 2-4 weeks was studied biochemically, histologically, and with MRI. Besides natural development, the response of the tissue to administration of retinoic acid, interleukin-1␤ (IL-1␤), and daily dosing with ascorbic acid was studied.Results. Tissue wet and dry weight, glycosaminoglycan (GAG) content, and collagen content all increased with development time, while tissue hydration decreased. The administration of retinoic acid resulted in a significant reduction in tissue wet weight, proteoglycan content, and cell number and an increase in hydration as compared with controls. Daily dosing with ascorbic acid increased tissue collagen content significantly compared with controls, while the administration of IL-1␤ resulted in increased proteoglycan content. The water proton longitudinal and transverse relaxation rates correlated well with GAG and collagen concentrations of the matrix as well as with tissue hydration. In contrast, the magnetization transfer value for the tissue correlated only with total collagen. Finally, the selfdiffusion coefficient of water correlated with tissue hydration.Conclusion. Parameters derived from MR images obtained noninvasively can be used to quantitatively assess the composition of cartilage tissue generated in a bioreactor. We conclude that MRI is a promising modality for the assessment of certain biochemical properties of cartilage in a wide variety of settings.

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Action of Compression and Cations on the Proton and Deuterium Relaxation in Cartilage

Cited by 65 publications

References 16 publications

Sodium and proton MR properties of cartilage during compression

Sodium and proton MR properties of cartilage during compression

Characterization of Engineered Cartilage Constructs Using MultiexponentialT₂Relaxation Analysis and Support Vector Regression

Response of engineered cartilage tissue to biochemical agents as studied by proton magnetic resonance microscopy

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Action of Compression and Cations on the Proton and Deuterium Relaxation in Cartilage

Cited by 65 publications

References 16 publications

Sodium and proton MR properties of cartilage during compression

Sodium and proton MR properties of cartilage during compression

Characterization of Engineered Cartilage Constructs Using MultiexponentialT2Relaxation Analysis and Support Vector Regression

Response of engineered cartilage tissue to biochemical agents as studied by proton magnetic resonance microscopy

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Product

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Characterization of Engineered Cartilage Constructs Using MultiexponentialT₂Relaxation Analysis and Support Vector Regression