Improved specificity of cartilage matrix evaluation using multiexponential transverse relaxation analysis applied to pathomimetically degraded cartilage

Evaluation of enzymatic proteoglycan loss and collagen degradation in human articular cartilage using ultrashort echo time-based biomarkers: A feasibility study

The objective of the current study was to investigate the feasibility of quantitative 3D ultrashort echo time (UTE)-based biomarkers in detecting proteoglycan (PG) loss and collagen degradation in human cartilage. A total of 104 cartilage samples were harvested for a trypsin digestion study (n = 44), and a sequential trypsin and collagenase digestion study (n = 60), respectively. Forty-four cartilage samples were randomly divided into a trypsin digestion group (tryp group) and a control group (phosphate-buffered saline [PBS] group) (n = 22 for each group) for the trypsin digestion experiment. The remaining 60 cartilage samples were divided equally into four groups (n = 15 for each group) for sequential trypsin and collagenase digestion, including PBS + Tris (incubated in PBS, then Tris buffer solution), PBS + 30 U col (incubated in PBS, then 30 U/ml collagenase [30 U col] with Tris buffer solution), tryp + 30 U col (incubated in trypsin solution, then 30 U/ml collagenase with Tris buffer solution), and tryp + Tris (incubated in trypsin solution, then Tris buffer solution). The 3D UTE-based MRI biomarkers included T₁ , multiecho T₂ *, adiabatic T_1ρ (AdiabT_1ρ ), magnetization transfer ratio (MTR), and modeling of macromolecular proton fraction (MMF). For each cartilage sample, UTE-based biomarkers (T₁ , T₂ *, AdiabT_1ρ , MTR, and MMF) and sample weight were evaluated before and after treatment. PG and hydroxyproline assays were performed. Differences between groups and correlations were assessed. All the evaluated biomarkers were able to differentiate between healthy and degenerated cartilage in the trypsin digestion experiment, but only T₁ and AdiabT_1ρ were significantly correlated with the PG concentration in the digestion solution (p = 0.004 and p = 0.0001, respectively). In the sequential digestion experiment, no significant differences were found for T₁ and AdiabT_1ρ values between the PBS + Tris and PBS + 30 U col groups (p = 0.627 and p = 0.877, respectively), but T₁ and AdiabT_1ρ values increased significantly in the tryp + Tris (p = 0.031 and p = 0.024, respectively) and tryp + 30 U col groups (both p < 0.0001). Significant decreases in MMF and MTR were found in the tryp + 30 U col group compared with the PBS + Tris group (p = 0.002 and p = 0.001, respectively). It was concluded that AdiabT_1ρ and T₁ have the potential for detecting PG loss, while MMF and MTR are promising for the detection of collagen degradation in articular cartilage, which could facilitate earlier, noninvasive diagnosis of osteoarthritis.

Bi-component T2 mapping correlates with articular cartilage material properties

Non-invasive estimation of cartilage material properties is useful for understanding cartilage health and creating subject-specific computational models. Bi-component T2 mapping measured using Multi-Component Driven Equilibrium Single Shot Observation of T₁ and T₂ (mcDESPOT) is sensitive for detecting cartilage degeneration within the human knee joint, but has not been correlated with cartilage composition and mechanical properties. Therefore, the purpose of this study was to investigate the relationship between bi-component T₂ parameters measured using mcDESPOT at 3.0 T and cartilage composition and mechanical properties. Ex-vivo patellar cartilage specimens harvested from five human cadaveric knees were imaged using mcDESPOT at 3.0 T. Cartilage samples were removed from the patellae, mechanically tested to determine linear modulus and dissipated energy, and chemically tested to determine proteoglycan and collagen content. Parameter maps of single-component T₂ relaxation time (T₂), the T₂ relaxation times of the fast relaxing macromolecular bound water component (T_2F) and slow relaxing bulk water component (T_2S), and the fraction of the fast relaxing macromolecular bound water component (F_F) were compared to mechanical and chemical measures using linear regression. F_F was significantly (p < 0.05) correlated with energy dissipation and linear modulus. T₂ was significantly (p ≤ 0.05) correlated with elastic modulus at 1 Hz and energy dissipated at all frequencies. There were no other significant (p = 0.13-0.97) correlations between mcDESPOT parameters and mechanical properties. F_F was significantly (p = 0.04) correlated with proteoglycan content. There were no other significant (p = 0.19-0.92) correlations between mcDESPOT parameters and proteoglycan or collagen content. This study suggests that F_F measured using mcDESPOT at 3.0 T could be used to non-invasively estimate cartilage proteoglycan content, elastic modulus, and energy dissipation.

Phase-based T2 mapping with gradient echo imaging

PURPOSE

Transverse relaxation time (T₂ ) mapping with MRI has a plethora of clinical and research applications. Current T₂ mapping techniques are based primarily on spin-echo (SE) relaxometry strategies that rely on the signal magnitude, and often suffer from lengthy acquisition times. In this work, we propose a phase-based T₂ mapping technique where T₂ information is encoded into the signal phase of rapid gradient echo (GRE) acquisitions.

THEORY

Bloch equation simulations demonstrate that the phase of GRE acquisitions obtained with a very small inter-repetition RF phase increment has a strong monotonic dependence on T₂ , resulting from coherent transverse magnetization. This T₂ -dependent phase behavior forms the basis of the proposed T₂ mapping technique. To isolate T₂ -dependent phase from background phase, at least 2 data sets with different RF phase increments are acquired. The proposed method can also be combined with chemical shift encoded MRI to separate water and fat signals.

METHODS

The feasibility of the proposed technique was validated in a phantom experiment. In vivo feasibility was demonstrated in the brain, knee, abdomen, and pelvis. Comparisons were made with SE-based T₂ mapping, spectroscopy, and T₂ values from the literature.

RESULTS

The proposed method produced accurate T₂ maps compared with SE-based T₂ mapping in the phantom. Good qualitative agreement was observed in vivo between the proposed method and the reference. T₂ measured in various anatomies agreed well with values reported in the literature.

CONCLUSION

A phase-based T₂ mapping technique was developed and its feasibility demonstrated in phantoms and in vivo.

Stabilization of T2 relaxation and magnetization transfer in cartilage explants by immersion in perfluorocarbon liquid

PURPOSE

Magnetic resonance imaging of ex vivo cartilage measures parameters such as T₂ and magnetization transfer ratio (MTR), which reflect structural changes associated with osteoarthritis. Samples are often immersed in aqueous solutions to prevent dehydration and to to improve susceptibility matching. This study sought to determine the extent to which T₂ and MTR changes are attributable to immersion alone and to identify immersion conditions to minimize this confounding factor.

METHODS

T₂ and MTR were measured before and after immersion for up to 24 hours at 4°C. Bovine nasal and articular cartilage and human articular cartilage were studied. Experimental groups included undisturbed immersion in Fluorinert FC-770, a susceptibility-matched, hydrophobic liquid with minimal tissue penetration, and immersion in Fluorinert, Dulbecco's phosphate-buffered saline (DPBS), or saline, with removal from the magnet between scans. ¹⁹ F and ¹ H-MRI were used to detect cartilage penetration by Fluorinert and swelling, respectively.

RESULTS

Saline and DPBS immersion rapidly increased T₂ , wet weight and cartilage volume and decreased MTR, suggesting increased water content for all cartilage types. Fluorinert-immersed samples exhibited minimal changes in T₂ or MTR. No ingress of Fluorinert was detected after 2 weeks of continuous immersion at 4°C.

CONCLUSION

Ex vivo quantitative MR studies of cartilage may be confounded by the effects of immersion in aqueous solution, which may be comparable to or larger than effects attributed to pathology. These effects may be mitigated by immersion in perfluorocarbon liquids such as Fluorinert FC-770.

Quantifying NMR relaxation correlation and exchange in articular cartilage with time domain analysis

Measured nuclear magnetic resonance (NMR) transverse relaxation data in articular cartilage has been shown to be multi-exponential and correlated to the health of the tissue. The observed relaxation rates are dependent on experimental parameters such as solvent, data acquisition methods, data analysis methods, and alignment to the magnetic field. In this study, we show that diffusive exchange occurs in porcine articular cartilage and impacts the observed relaxation rates in T₁-T₂ correlation experiments. By using time domain analysis of T₂-T₂ exchange spectroscopy, the diffusive exchange time can be quantified by measurements that use a single mixing time. Measured characteristic times for exchange are commensurate with T₁ in this material and so impacts the observed T₁ behavior. The approach used here allows for reliable quantification of NMR relaxation behavior in cartilage in the presence of diffusive fluid exchange between two environments.

Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint

PURPOSE

We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods.

MATERIALS AND METHODS

Multicomponent driven equilibrium single-pulse observation of T₁ and T₂ (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects.

RESULTS

Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject.

CONCLUSIONS

BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF.

Assessment of different fitting methods for in-vivo bi-component T2* analysis of human patellar tendon in magnetic resonance imaging

PURPOSE

To investigate the robustness of four fitting methods for bi-component effective spin-spin T2 (T2*) relaxation time analysis of human patellar tendon.

METHODS

A three-dimensional (3D) cone ultra-short echo-time (UTE) sequence was performed on the knees of ten healthy volunteers at 3.0T. Four fitting methods incorporating either Gaussian or Rician noise distribution were used for voxel-by-voxel bi-component T2* analysis of the patellar tendon. The T2* for the short relaxing (T**,s ) and long relaxing (T*2,l ) water components and the fraction of the short relaxing water component (fs ) were measured, and different fitting methods were compared using Friedman's and Wilcoxon signed rank tests. A numerical simulation study was also performed to predict the accuracy and precision of bi-component T2* parameter estimation in tendon at different signal-to-noise ratios (SNR) levels.

RESULTS

The average T*2,s , T*2,l , fs of human patellar tendon were 1.5ms, 30ms, and 80% respectively. Incorporating different noise models and fitting methods influenced the measured bi-component T2* parameters. Fitting methods incorporating Rician noise were superior to traditional fitting methods for bi-component T2* analysis especially at lower SNR. fs and T*2,s were less sensitive than T*2,1 to noise at even moderate and low SNR. The result of the in-vivo bi-component T2* analysis of tendon agreed well with numerical simulations.

CONCLUSION

Our study demonstrated the use of a 3D cone UTE sequence to perform in vivo voxel-by-voxel bi-component T2* analysis of human patellar tendon. Incorporating Rician noise was useful for improving bi-component T2* analysis especially at lower SNR.

LEVEL OF EVIDENCE

IV.

Anomalous T2 relaxation in normal and degraded cartilage

PURPOSE

To compare the ordinary monoexponential model with three anomalous relaxation models-the stretched Mittag-Leffler, stretched exponential, and biexponential functions-using both simulated and experimental cartilage relaxation data.

METHODS

Monte Carlo simulations were used to examine both the ability of identifying a given model under high signal-to-noise ratio (SNR) conditions and the accuracy and precision of parameter estimates under more modest SNR as would be encountered clinically. Experimental transverse relaxation data were analyzed from normal and enzymatically degraded cartilage samples under high SNR and rapid echo sampling to compare each model.

RESULTS

Both simulation and experimental results showed improvement in signal representation with the anomalous relaxation models. The stretched exponential model consistently showed the lowest mean squared error in experimental data and closely represents the signal decay over multiple decades of the decay time (e.g., 1-10 ms, 10-100 ms, and >100 ms). The stretched exponential parameter αse showed an inverse correlation with biochemically derived cartilage proteoglycan content.

CONCLUSION

Experimental results obtained at high field suggest potential application of αse as a measure of matrix integrity. Simulation reflecting more clinical imaging conditions, indicate the ability to robustly estimate αse and distinguish between normal and degraded tissue, highlighting its potential as a biomarker for human studies. Magn Reson Med 76:953-962, 2016. © 2015 Wiley Periodicals, Inc.

Monitoring the Progression of Spontaneous Articular Cartilage Healing with Infrared Spectroscopy

OBJECTIVE

Evaluation of early compositional changes in healing articular cartilage is critical for understanding tissue repair and for therapeutic decision-making. Fourier transform infrared imaging spectroscopy (FT-IRIS) can be used to assess the molecular composition of harvested repair tissue. Furthermore, use of an infrared fiber-optic probe (IFOP) has the potential for translation to a clinical setting to provide molecular information in situ. In the current study, we determined the feasibility of IFOP assessment of cartilage repair tissue in a rabbit model, and assessed correlations with gold-standard histology.

DESIGN

Bilateral osteochondral defects were generated in mature white New Zealand rabbits, and IFOP data obtained from defect and adjacent regions at 2, 4, 6, 8, 12, and 16 weeks postsurgery. Tissues were assessed histologically using the modified O'Driscoll score, by FT-IRIS, and by partial least squares (PLS) modeling of IFOP spectra.

RESULTS

The FT-IRIS parameters of collagen content, proteoglycan content, and collagen index correlated significantly with modified O'Driscoll score (P = 0.05, 0.002, and 0.02, respectively), indicative of their sensitivity to tissue healing. Repair tissue IFOP spectra were distinguished from normal tissue IFOP spectra in all samples by PLS analysis. However, the PLS model for prediction of histological score had a high prediction error, which was attributed to the spectral information being acquired from the tissue surface only.

CONCLUSION

The strong correlations between FT-IRIS data and histological score support further development of the IFOP technique for clinical applications, although further studies to optimize data collection from the full sample depths are required.

Assessment of chemical exchange in tryptophan-albumin solution through (19)F multicomponent transverse relaxation dispersion analysis

A number of NMR methods possess the capability of probing chemical exchange dynamics in solution. However, certain drawbacks limit the applications of these NMR approaches, particularly, to a complex system. Here, we propose a procedure that integrates the regularized nonnegative least squares (NNLS) analysis of multiexponential T2 relaxation into Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion experiments to probe chemical exchange in a multicompartmental system. The proposed procedure was validated through analysis of (19)F T2 relaxation data of 6-fluoro-DL-tryptophan in a two-compartment solution with and without bovine serum albumin. Given the regularized NNLS analysis of a T2 relaxation curve acquired, for example, at the CPMG frequency υ CPMG  = 125, the nature of two distinct peaks in the associated T2 distribution spectrum indicated 6-fluoro-DL-tryptophan either retaining the free state, with geometric mean */multiplicative standard deviation (MSD) = 1851.2 ms */1.51, or undergoing free/albumin-bound interconversion, with geometric mean */MSD = 236.8 ms */1.54, in the two-compartment system. Quantities of the individual tryptophan species were accurately reflected by the associated T2 peak areas, with an interconversion state-to-free state ratio of 0.45 ± 0.11. Furthermore, the CPMG relaxation dispersion analysis estimated the exchange rate between the free and albumin-bound states in this fluorinated tryptophan analog and the corresponding dissociation constant of the fluorinated tryptophan-albumin complex in the chemical-exchanging, two-compartment system.

Articular Cartilage of the Human Knee Joint: In Vivo Multicomponent T2 Analysis at 3.0 T

PURPOSE

To compare multicomponent T2 parameters of the articular cartilage of the knee joint measured by using multicomponent driven equilibrium single-shot observation of T1 and T2 (mcDESPOT) in asymptomatic volunteers and patients with osteoarthritis.

MATERIALS AND METHODS

This prospective study was performed with institutional review board approval and with written informed consent from all subjects. The mcDESPOT sequence was performed in the knee joint of 13 asymptomatic volunteers and 14 patients with osteoarthritis of the knee. Single-component T2 (T2(Single)), T2 of the fast-relaxing water component (T2F) and of the slow-relaxing water component (T2S), and the fraction of the fast-relaxing water component (F(F)) of cartilage were measured. Wilcoxon rank-sum tests and multivariate linear regression models were used to compare mcDESPOT parameters between volunteers and patients with osteoarthritis. Receiver operating characteristic analysis was used to assess diagnostic performance with mcDESPOT parameters for distinguishing morphologically normal cartilage from morphologically degenerative cartilage identified at magnetic resonance imaging in eight cartilage subsections of the knee joint.

RESULTS

Higher cartilage T2(Single) (P < .001), lower cartilage F(F) (P < .001), and similar cartilage T2F (P = .079) and T2S (P = .124) values were seen in patients with osteoarthritis compared with those in asymptomatic volunteers. Differences in T2(Single) and F(F) remained significant (P < .05) after consideration of age differences between groups of subjects. Diagnostic performance was higher with F(F) than with T2(Single) for distinguishing between normal and degenerative cartilage (P < .05), with greater areas under the curve at receiver operating characteristic analysis.

CONCLUSION

Patients with osteoarthritis of the knee had significantly higher cartilage T2(Single) and significantly lower cartilage F(F) than did asymptomatic volunteers, and receiver operating characteristic analysis results suggested that F(F) may allow greater diagnostic performance than that with T2(Single) for distinguishing between normal and degenerative cartilage.

Quantitative magnetic resonance imaging of the articular cartilage of the knee joint

Osteoarthritis is characterized by a decrease in the proteoglycan content and disruption of the highly organized collagen fiber network of articular cartilage. Various quantitative magnetic resonance imaging techniques have been developed for noninvasive assessment of the proteoglycan and collagen components of cartilage. These techniques have been extensively used in clinical practice to detect early cartilage degeneration and in osteoarthritis research studies to monitor disease-related and treatment-related changes in cartilage over time. This article reviews the role of quantitative magnetic resonance imaging in evaluating the composition and ultrastructure of the articular cartilage of the knee joint.

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions

BACKGROUND

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images.

METHODS

Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC).

RESULTS

The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies.

CONCLUSION

Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules.

Bayesian analysis of transverse signal decay with application to human brain

PURPOSE

Transverse relaxation analysis with several signal models has been used extensively to determine tissue and material properties. However, the derivation of corresponding parameter values is notoriously unreliable. We evaluate improvements in the quality of parameter estimation using Bayesian analysis and incorporating the Rician noise model, as appropriate for magnitude MR images.

THEORY AND METHODS

Monoexponential, stretched exponential, and biexponential signal models were analyzed using nonlinear least squares (NLLS) and Bayesian approaches. Simulations and phantom and human brain data were analyzed using three different approaches to account for noise. Parameter estimation bias (reflecting accuracy) and dispersion (reflecting precision) were derived for a range of signal-to-noise ratios (SNR) and relaxation parameters.

RESULTS

All methods performed well at high SNR. At lower SNR, the Bayesian approach yielded parameter estimates of considerably greater precision, as well as greater accuracy, than did NLLS. Incorporation of the Rician noise model greatly improved accuracy and, to a somewhat lesser extent, precision, in derived transverse relaxation parameters. Analyses of data obtained from solution phantoms and from brain were consistent with simulations.

CONCLUSION

Overall, estimation of parameters characterizing several different transverse relaxation models was markedly improved through use of Bayesian analysis and through incorporation of the Rician noise model.

Prediction of cartilage compressive modulus using multiexponential analysis of T(2) relaxation data and support vector regression

Evaluation of mechanical characteristics of cartilage by magnetic resonance imaging would provide a noninvasive measure of tissue quality both for tissue engineering and when monitoring clinical response to therapeutic interventions for cartilage degradation. We use results from multiexponential transverse relaxation analysis to predict equilibrium and dynamic stiffness of control and degraded bovine nasal cartilage, a biochemical model for articular cartilage. Sulfated glycosaminoglycan concentration/wet weight (ww) and equilibrium and dynamic stiffness decreased with degradation from 103.6 ± 37.0 µg/mg ww, 1.71 ± 1.10 MPa and 15.3 ± 6.7 MPa in controls to 8.25 ± 2.4 µg/mg ww, 0.015 ± 0.006 MPa and 0.89 ± 0.25MPa, respectively, in severely degraded explants. Magnetic resonance measurements were performed on cartilage explants at 4 °C in a 9.4 T wide-bore NMR spectrometer using a Carr-Purcell-Meiboom-Gill sequence. Multiexponential T2 analysis revealed four water compartments with T2 values of approximately 0.14, 3, 40 and 150 ms, with corresponding weight fractions of approximately 3, 2, 4 and 91%. Correlations between weight fractions and stiffness based on conventional univariate and multiple linear regressions exhibited a maximum r(2) of 0.65, while those based on support vector regression (SVR) had a maximum r(2) value of 0.90. These results indicate that (i) compartment weight fractions derived from multiexponential analysis reflect cartilage stiffness and (ii) SVR-based multivariate regression exhibits greatly improved accuracy in predicting mechanical properties as compared with conventional regression.

New methods to study the composition and structure of the extracellular matrix in natural and bioengineered tissues

The extracellular matrix (ECM) comprises a gel of numerous biopolymers that occurs in a multitude of biological tissues. The ECM provides the basic support and mechanical strength of skeletal tissue and is responsible for shape retention. At the same time, the ECM is responsible for the viscoelastic properties and the elasticity of soft tissues. As expected, there are several important diseases that affect and degenerate the ECM with severe consequences for its properties. Bioengineering is a promising approach to support the regenerative capacity of the body. Unfortunately, the biomechanical properties of bioengineered ECM often only poorly meet the standards of their native counterparts. Many bioengineered tissues are characterized by an increased glycosaminoglycan (GAG) but decreased collagen content. This leads to an enhanced water content that strongly alters the viscoelastic and thus the biomechanical properties. Therefore, compositional analysis is important to estimate the tissue quality. We will show that nuclear magnetic resonance (NMR) spectroscopy and soft-ionization mass spectrometry (MS) represent useful techniques for ECM research both in natural and bioengineered tissues. Both methods are strongly complimentary: while MS techniques such as matrix-assisted laser desorption and ionization (MALDI) are excellent and very sensitive analytical tools to determine the collagen and the GAG contents of tissues, NMR spectroscopy provides insight into the molecular architecture of the ECM, its dynamics and other important parameters such as the water content of the tissue as well as the diffusion of molecules within the ECM.

Incorporation of Rician noise in the analysis of biexponential transverse relaxation in cartilage using a multiple gradient echo sequence at 3 and 7 Tesla

PURPOSE

Previous work has evaluated the quality of different analytic methods for extracting relaxation times from magnitude imaging data exhibiting Rician noise. However, biexponential analysis of relaxation in tissue, including cartilage, and materials is of increasing interest. We, therefore, analyzed biexponential transverse relaxation decay in the presence of Rician noise and assessed the accuracy and precision of several approaches to determining component fractions and apparent transverse relaxation times.

THEORY AND METHODS

Comparisons of four different voxel-by-voxel fitting methods were performed using Monte Carlo simulations, and phantom and ex vivo bovine nasal cartilage (BNC) experiments. In each case, preclinical and clinical imaging field strengths of 7 Tesla (T) and 3T, respectively, and parameters, were investigated across a range of signal-to-noise ratios (SNR). Results were compared with Cramér-Rao lower bound calculations.

RESULTS

As expected, at high SNR, all methods performed well. At lower SNR, fits explicitly incorporating the analytic form of the Rician noise maintained performance. The much more efficient correction scheme of Gudbjartsson and Patz performed almost as well in many cases. Ex vivo experiments on phantoms and BNC were consistent with simulation results.

CONCLUSION

Explicit incorporation of Rician noise greatly improves accuracy and precision in the analysis of biexponential transverse decay data.

Quantitative MRI of articular cartilage and its clinical applications

Cartilage is one of the most essential tissues for healthy joint function and is compromised in degenerative and traumatic joint diseases. There have been tremendous advances during the past decade using quantitative MRI techniques as a noninvasive tool for evaluating cartilage, with a focus on assessing cartilage degeneration during osteoarthritis (OA). In this review, after a brief overview of cartilage composition and degeneration, we discuss techniques that grade and quantify morphologic changes as well as the techniques that quantify changes in the extracellular matrix. The basic principles, in vivo applications, advantages, and challenges for each technique are discussed. Recent studies using the OA Initiative (OAI) data are also summarized. Quantitative MRI provides noninvasive measures of cartilage degeneration at the earliest stages of joint degeneration, which is essential for efforts toward prevention and early intervention in OA.

Experimental issues in the measurement of multi-component relaxation times in articular cartilage by microscopic MRI

A number of experimental issues in the measurement of multi-component T2 and T1ρ relaxations in native and enzymatically digested articular cartilage were investigated by microscopic MRI (μMRI). The issues included the bath solutions (physiological saline and phosphate buffered saline (PBS)), the imaging resolution (35-140 μm), the specimen orientations (0° and 55°), and the strength of spin-lock frequencies (0.5-2 kHz) in the T1ρ experiments. In addition to cartilage, the samples of agar gel and doped water solution were also used in the investigation. Two imaging sequences were used: CPMG-SE and MSME. All raw data were analyzed by the non-negative least square (NNLS) method. The MSME sequence was shown to result in the observation of multi-component T2, even in the gel and liquid samples, demonstrating the artificial uncleanness of this sequence in the multi-component measurements. The soaking of cartilage in PBS reduced the observable T2 components to one at both 0° and 55°, suggesting the effect of phosphate ions on proton exchange between different pools of water molecules. The cartilage orientation with respect to the external magnetic field and the spin-lock strengths in the T1ρ experiment both affected the quantification of the multi-component relaxation. The transitions between a mono-component and multi-components in cartilage under various experimental conditions call for the extra caution in interpreting the relaxation results.

Rapid multicomponent T2 analysis of the articular cartilage of the human knee joint at 3.0T

PURPOSE

To determine the feasibility of using multicomponent-driven equilibrium single-shot observation of T1 and T2 (mcDESPOT) for evaluating the human knee joint at 3.0T and to investigate depth-dependent and regional-dependent variations in multicomponent T2 parameters within articular cartilage.

MATERIALS AND METHODS

mcDESPOT was performed on the knee joint of 10 asymptomatic volunteers at 3.0T. Single-component T2 relaxation time (T2single ), multicomponent T2 relaxation time for water tightly bound to proteoglycan (T2PG ) and bulk water loosely bound to the macromolecular matrix (T2BW ), and fraction of water tightly bound to proteoglycan (FPG ) were measured in eight cartilage subsections and within the superficial and deep layers of patellar cartilage. Statistical analysis was used to investigate depth-dependent and regional-dependent variations in parameters.

RESULTS

There was lower (P = 0.001) T2single and T2PG and higher (P < 0.001) FPG in the deep than superficial layer of patellar cartilage. There was higher (P < 0.001) FPG on the weight-bearing surfaces than nonweight-bearing surfaces. There was higher (P < 0.001) T2single , T2PG , and T2BW on the trochlea and posterior medial and lateral femoral condyles than the patella, central medial and lateral femoral condyles, and medial and lateral tibia plateaus.

CONCLUSION

Multicomponent T2 parameters of the articular cartilage of the human knee joint can be measured at 3.0T using mcDESPOT and show depth-dependent and regional-dependent variations.

Radiofrequency (RF) coil impacts the value and reproducibility of cartilage spin-spin (T2) relaxation time measurements

INTRODUCTION

T2 (spin-spin) relaxation time is frequently used for compositional assessment of articular cartilage. However little is known about the influence of magnetic resonance (MR) system components on these measurements. The reproducibility and range of cartilage T2 values were evaluated using different extremity radiofrequency (RF) coils with potential differences in flip angle uniformity and signal-to-noise ratio (SNR).

METHOD

Ten knees underwent 3 T MR exams using RF coils with different SNR: quadrature transmit/receive (QTR); quadrature transmit/eight-channel phased-array receive (QT8PAR). Each knee was scanned twice per coil (four exams total). T2 values were calculated for the central medial and lateral femoral (cMF, cLF) and medial and lateral tibial (MT, LT) cartilage.

RESULTS

The flip angle varied across a central 40 mm diameter region-of-interest of each coil by <1.5%. However SNR was significantly higher using QT8PAR than QTR (P < 0.001). T2 values for cMF (50.7 msec/45.9 msec) and MT (48.2 msec/41.6 msec) were significantly longer with QT8PAR than QTR (P < 0.05). T2 reproducibility was improved using QT8PAR for cMF and cLF (4.8%/5.8% and 4.1%/6.5%; P < 0.001), similar for LT (3.8%/3.6%; P = 1.0), and worse for MT (3.7%/3.3%; P < 0.001). T2 varied spatially, with cLF having the longest (52.0 msec) and the LT having the shortest (40.6 msec) values. All deep cartilage had significantly longer, and less variable, T2 values using QT8PAR (higher SNR; P < 0.03).

CONCLUSIONS

SNR varied spatially (significant) depending upon coil, but refocusing flip angle only slightly. With higher SNR, significantly longer T2 values were measured for deep (all plates) and global (MT, cMF) cartilage. T2 values varied by depth and plate, in agreement with prior studies.

Anisotropic analysis of multi-component T2 and T1ρ relaxations in achilles tendon by NMR spectroscopy and microscopic MRI

PURPOSE

To study the anisotropic characteristics of both multi-component T2 and T1ρ relaxation times in tendon.

MATERIALS AND METHODS

T2 and T1ρ were measured in tendon by NMR spectroscopy at different orientations and by microscopic MRI at the magic angle. Several experimental issues in the multi-component relaxation measurements were investigated, including the effects of echo spacing, the resolution of MRI experiments, the influence of the specimen orientations, and the strengths of different spin-lock frequencies in T1ρ experiments.

RESULTS

Both the values and fractions of T2 in tendon showed significant orientational dependence. The values and fractions of T1ρ strongly depended on both the specimen orientation and the spin-lock strength. The imaging resolution (35-280 μm) had little influence in the T2 experiments. Both the echo spacings (0.6-3.0 ms) in the T2 experiment and the spin-lock strengths (0.5-5 kHz) in the T1ρ experiment affected the quantification of the multi-component relaxation. Up to three T2 and T1ρ components were resolved in tendon.

CONCLUSION

Multi-component relaxations could be attributed to different populations of water in the tissue. The transitions between a mono-component and multi-component result call for the caution in interpreting the relaxation results.

Improved MR-based characterization of engineered cartilage using multiexponential T2 relaxation and multivariate analysis

Noninvasive monitoring of tissue quality would be of substantial use in the development of cartilage tissue engineering strategies. Conventional MR parameters provide noninvasive measures of biophysical tissue properties and are sensitive to changes in matrix development, but do not clearly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with particular changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for the evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 and 5 weeks of development, respectively. First, we applied multiexponential T(2) analysis for the quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine analysis using multiple MR parameters to improve detection of degree of matrix development. Monoexponential T(2) values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T(2) distribution in both immature and mature constructs was qualitatively comparable to that of native cartilage. The analysis showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05 ± 0.01 to 0.07 ± 0.01. Classification of samples based on individual MR parameters, T(1), T(2), k(m) or apparent diffusion coefficient, showed that the best classifiers were T(1) and k(m), with classification accuracies of 85% and 84%, respectively. Support vector machine analysis improved the accuracy to 98% using the combination (k(m), apparent diffusion coefficient). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopic analyses, which showed increased proteoglycan and collagen with maturation. In summary, multiexponential T(2) and multivariate support vector machine analyses provide improved sensitivity to changes in matrix development and specificity to matrix composition in tissue engineered cartilage. These approaches show substantial potential for the evaluation of engineered cartilage tissue and for extension to other tissue engineering constructs.

Characterization of engineered cartilage constructs using multiexponential T₂ relaxation analysis and support vector regression

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 μg/mg in week 1 to 21.06±4.14 μg/mg in week 4. Using multiexponential T₂ analysis, we detected at least three distinct water compartments, with T₂ 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 T₂ 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 T₂ from monoexponential fitting and sGAG exhibited a correlation coefficient of r²=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 r²=0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r² in the range 0.43-0.54. However, correlations using SVR exhibited r² 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 T₂ 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.