Anomalous T2 relaxation in normal and degraded cartilage

Quantitative T2 mapping monitoring the maturation of engineered elastic cartilage in a rabbit model

BACKGROUND

Cartilage tissue engineering provides a promising approach to reconstruct craniofacial defects, and a noninvasive method is needed to assess its effectiveness. Although magnetic resonance imaging (MRI) has been used to evaluate articular cartilage in vivo, few studies focused on its feasibility in monitoring engineered elastic cartilage (EC).

METHODS

Auricular cartilage, silk fibroin (SF) scaffold, and EC consisting of rabbit auricular chondrocytes and SF scaffold were transplanted subcutaneously into the rabbit back. In eight weeks after transplantation, grafts were imaged by MRI using PROSET, PDW VISTA SPAIR, 3D T2 VISTA, 2D MIXED T2 Multislice, and SAG TE multiecho sequences, followed by histological examination and biochemical analysis. Statistical analyses were performed to identify the association between T2 values and biochemical indicator values of EC.

RESULTS

In vivo imaging shows that 2D MIXED T2 Multislice sequence (T2 mapping) clearly distinguished the native cartilage, engineered cartilage and fibrous tissue. T2 values showed high correlations with cartilage-specific biochemical parameters at different time points, especially the elastic cartilage specific protein elastin (ELN, r= -0.939, P < 0.001).

CONCLUSION

Quantitative T2 mapping can effectively detect the in vivo maturity of engineered elastic cartilage after subcutaneously transplantation. This study would promote the clinical application of MRI T2 mapping in monitoring engineered elastic cartilage in the repair of craniofacial defects.

Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation

Many methods have been developed for estimating the parameters of biexponential decay signals, which arise throughout magnetic resonance relaxometry (MRR) and the physical sciences. This is an intrinsically ill-posed problem so that estimates can depend strongly on noise and underlying parameter values. Regularization has proven to be a remarkably efficient procedure for providing more reliable solutions to ill-posed problems, while, more recently, neural networks have been used for parameter estimation. We re-address the problem of parameter estimation in biexponential models by introducing a novel form of neural network regularization which we call input layer regularization (ILR). Here, inputs to the neural network are composed of a biexponential decay signal augmented by signals constructed from parameters obtained from a regularized nonlinear least-squares estimate of the two decay time constants. We find that ILR results in a reduction in the error of time constant estimates on the order of 15%-50% or more, depending on the metric used and signal-to-noise level, with greater improvement seen for the time constant of the more rapidly decaying component. ILR is compatible with existing regularization techniques and should be applicable to a wide range of parameter estimation problems.

Quantitative T2 and T1ρ mapping are sensitive to ischemic injury to the epiphyseal cartilage in an in vivo piglet model of Legg-Calvé-Perthes disease

OBJECTIVE

To determine if the quantitative MRI techniques T2 and T1ρ mapping are sensitive to ischemic injury to epiphyseal cartilage in vivo in a piglet model of Legg-Calvé-Perthes disease using a clinical 3T MRI scanner. We hypothesized that T2 and T1ρ relaxation times would be increased in the epiphyseal cartilage of operated vs contralateral-control femoral heads 1 week following onset of ischemia.

DESIGN

Unilateral femoral head ischemia was surgically induced in eight piglets. Piglets were imaged 1 week post-operatively in vivo at 3T MRI using a magnetization-prepared 3D fast spin echo sequence for T2 and T1ρ mapping and a 3D gradient echo sequence for cartilage segmentation. Ischemia was confirmed in all piglets using gadolinium contrast-enhanced MRI. Median T2 and T1ρ relaxation times were measured in the epiphyseal cartilage of the ischemic and control femoral heads and compared using paired t-tests. Histological assessment was performed on a subset of five piglets.

RESULTS

T2 and T1ρ relaxation times were significantly increased in the epiphyseal cartilage of the operated vs control femoral heads (ΔT2 = 11.9 ± 3.7 ms, 95% CI = [8.8, 15.0] ms, P < 0.0001; ΔT1ρ = 12.8 ± 4.1 ms, 95% CI = [9.4, 16.2] ms, P < 0.0001). Histological assessment identified chondronecrosis in the hypertrophic and deep proliferative zones within ischemic epiphyseal cartilage.

CONCLUSIONS

T2 and T1ρ mapping are sensitive to ischemic injury to the epiphyseal cartilage in vivo at clinical 3T MRI. These techniques may be clinically useful to assess injury and repair to the epiphyseal cartilage to better stage the extent of ischemic damage in Legg-Calvé-Perthes disease.

Optimization of spin-lock times in T1ρ mapping of knee cartilage: Cramér-Rao bounds versus matched sampling-fitting

PURPOSE

To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T_1ρ ) mapping.

METHODS

Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T_1ρ mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T_1ρ fitting methods for MSF approaches.

RESULTS

All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5-8.0% error), model phantoms (1.5-2.8% error), and healthy volunteers (7.7-21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2-13.3% error on synthetic data, 2.1-6.2% error on model phantoms, 9.8-27.8% error on healthy volunteers).

CONCLUSION

A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T_1ρ mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.

Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution

The fractional-order Bloch equations have been shown to describe a wider range of experimental situations involving heterogeneous, porous, or composite materials. This paper introduces a novel dictionary of quantitative MR fingerprinting generated by signal evolution model with fractional-order Bloch equations to describe magnetic resonance (MR) relaxation. Here, the fractional-order relaxation models are implemented into Bloch equations through phase transitions using EPG simulation. In the phantom experiments, the fractional-order analysis showed smaller root mean squared error (T1: RMSE = 5.21%, T2: RMSE=3.75%) using the proposed method compared to using conventional method. Among the in vivo experiments of human brains, the estimated T1 and T2 values (mean ± SD) were 843 ± 46.3 ms and 70 ± 4.7 ms in white matter, 1323 ± 28.5 ms and 95 ± 3.8 ms in gray matter. So the proposed method can provide well extensions of current MR fingerprinting and has shown potential to apply into the phantom experiments and the in vivo applications to approach the standard methods for quantitative imaging.

Parsimonious modeling of skeletal muscle perfusion: Connecting the stretched exponential and fractional Fickian diffusion

PURPOSE

To develop an anomalous (non-Gaussian) diffusion model for characterizing skeletal muscle perfusion using multi-b-value DWI.

THEORY AND METHODS

Fick's first law was extended for describing tissue perfusion as anomalous superdiffusion, which is non-Gaussian diffusion exhibiting greater particle spread than that of the Gaussian case. This was accomplished using a space-fractional derivative that gives rise to a power-law relationship between mean squared displacement and time, and produces a stretched exponential signal decay as a function of b-value. Numerical simulations were used to estimate parameter errors under in vivo conditions, and examine the effect of limited SNR and residual fat signal. Stretched exponential DWI parameters, α and D , were measured in thigh muscles of 4 healthy volunteers at rest and following in-magnet exercise. These parameters were related to a stable distribution of jump-length probabilities and used to estimate microvascular volume fractions.

RESULTS

Numerical simulations showed low dispersion in parameter estimates within 1.5% and 1%, and bias errors within 3% and 10%, for α and D , respectively. Superdiffusion was observed in resting muscle, and to a greater degree following exercise. Resting microvascular volume fraction was between 0.0067 and 0.0139 and increased between 2.2-fold and 4.7-fold following exercise.

CONCLUSIONS

This model captures superdiffusive molecular motions consistent with perfusion, using a parsimonious representation of the DWI signal, providing approximations of microvascular volume fraction comparable with histological estimates. This signal model demonstrates low parameter-estimation errors, and therefore holds potential for a wide range of applications in skeletal muscle and elsewhere in the body.

Fast multicomponent 3D-T1ρ relaxometry

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r = 0.70, p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.

Tissue-Specific T2 * Biomarkers in Patellar Tendinopathy by Subregional Quantification Using 3D Ultrashort Echo Time MRI

Background

Quantitative MRI of patellar tendinopathy (PT) can be challenging due to spatial variation of T₂* relaxation times.

Purpose

1) To compare T₂* quantification using a standard approach with analysis in specific tissue compartments of the patellar tendon. 2) To evaluate test–retest reliability of different methods for fitting ultrashort echo time (UTE)‐relaxometry data.

Study Type

Prospective.

Subjects

Sixty‐five athletes with PT.

Field Strength/Sequence

3D UTE scans covering the patellar tendon were acquired using a 3.0T scanner and a 16‐channel surface coil.

Assessment

Voxelwise median T₂* was quantified with monoexponential, fractional‐order, and biexponential fitting. We applied two methods for T₂* analysis: first, a standard approach by analyzing all voxels covering the proximal patellar tendon. Second, within subregions of the patellar tendon, by using thresholds on biexponential fitting parameter percentage short T₂* (0–30% for mostly long T₂*, 30–60% for mixed T₂*, and 60–100% for mostly short T₂*).

Statistical Tests

Average test–retest reliability was assessed in three athletes using coefficients‐of‐variation (CV) and coefficients‐of‐repeatability (CR).

Results

With standard image analysis, we found a median [interquartile range, IQR] monoexponential T₂* of 6.43 msec [4.32–8.55] and fractional order T₂* 4.39 msec [3.06–5.78]. The percentage of short T₂* components was 52.9% [35.5–69.6]. Subregional monoexponential T₂* was 13.78 msec [12.11–16.46], 7.65 msec [6.49–8.61], and 3.05 msec [2.52–3.60] and fractional order T₂* 11.82 msec [10.09–14.44], 5.14 msec [4.25–5.96], and 2.19 msec [1.82–2.64] for 0–30%, 30–60%, and 60–100% short T₂*, respectively. Biexponential component short T₂* was 1.693 msec [1.417–2.003] for tissue with mostly short T₂* and long T₂* of 15.79 msec [13.47–18.61] for mostly long T₂*. The average CR (CV) was 2 msec (15%), 2 msec (19%) and 10% (22%) for monoexponential, fractional order and percentage short T₂*, respectively.

Data Conclusion

Patellar tendinopathy is characterized by regional variability in binding states of water. Quantitative multicompartment T₂* analysis in PT can be facilitated using a voxel selection method based on using biexponential fitting parameters.

Level of Evidence

1

Technical Efficacy Stage

1 J. Magn. Reson. Imaging 2020;52:420–430.

Volumetric multicomponent T1ρ relaxation mapping of the human liver under free breathing at 3T

PURPOSE

To develop a 3D sequence for T_1ρ relaxation mapping using radial volumetric encoding (3D-T_1ρ -RAVE) and to evaluate the multi relaxation components in the liver of healthy controls and chronic liver disease (CLD) patients.

METHODS

Fat saturation and T_1ρ preparation modules were followed by a train of gradient-echo acquisitions and T₁ restoration delay. The series of T_1ρ -weighted images were fitted using mono-exponential, bi-exponential, and stretched-exponential models. The repeatability and reproducibility of the proposed technique were evaluated on National Institute of Standards and Technology phantom by calculating the coefficient of variation between test-retest scans on the same scanner and between two different 3T scanners, respectively. Mann-Whitney U-test was performed to assess differences in T_1ρ components among patients (n = 3) and a control group (n = 10).

RESULTS

The phantom study showed an error of 8.9% and 11.5% in mono T₂ relaxation time measurement relative to the reference on 2 different scanners. The coefficient of variation for test-retest scans performed on the same scanner was 5.7% and 2.4% for scans performed on 2 scanners. The comparison between healthy controls and CLD patients showed a significant difference (P < .05) in mono relaxation time (P = .002), stretched-exponential relaxation parameter (P = .04). The Akaike information criteria C criterion showed 2.53 ± 0.9% (2.3 ± 0.3% for CLD) of the voxels are bi-exponential while in 65.3 ± 5.8% (81.2 ± 0.06% for CLD) of the liver voxels, the stretched-exponential model was preferred.

CONCLUSION

The 3D-T_1ρ -RAVE sequence allows volumetric, multicomponent T_1ρ assessment of the liver during free breathing and can distinguish between healthy volunteers and CLD patients.

Accelerated mono- and biexponential 3D-T1ρ relaxation mapping of knee cartilage using golden angle radial acquisitions and compressed sensing

PURPOSE

To use golden-angle radial sampling and compressed sensing (CS) for accelerating mono- and biexponential 3D spin-lattice relaxation time in the rotating frame (T_1ρ ) mapping of knee cartilage.

METHODS

Golden-angle radial stack-of-stars and Cartesian 3D-T_1ρ -weighted knee cartilage datasets (n = 12) were retrospectively undersampled by acceleration factors (AFs) 2-10. CS-based reconstruction using 8 different sparsifying transforms were compared for mono- and biexponential T_1ρ -mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested.

RESULTS

Most CS methods performed well for an AF of 2, with relative median normalized absolute deviation below 10% for monoexponential and biexponential mapping. For monoexponential mapping, radial sampling obtained a median normalized absolute deviation below 10% up to AF of 10, while Cartesian obtained this level of error only up to AF of 4. Radial sampling was also better with biexponential T_1ρ mapping, with median normalized absolute deviation below 10% up to AF of 6.

CONCLUSION

Golden-angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D-T_1ρ mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.

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.

The γ-parameter of anomalous diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences

Motivated by previous results obtained in vitro, we investigated the dependence of the anomalous diffusion (AD) MRI technique on local magnetic susceptibility differences (Δχ) driven by magnetic field inhomogeneity in human brains. The AD-imaging contrast investigated here is quantified by the stretched-exponential parameter γ, extracted from diffusion weighted (DW) data collected by varying diffusion gradient strengths. We performed T₂* and DW experiments in eight healthy subjects at 3.0T. T₂*-weighted images at different TEs=(10,20,35,55)ms and DW-EPI images with fourteen b-values from 0 to 5000s/mm² were acquired. AD-metrics and Diffusion Tensor Imaging (DTI) parameters were compared and correlated to R₂* and to Δχ values taken from literature for the gray (GM) and the white (WM) matter. Pearson's correlation test and Analysis of Variance with Bonferroni post-hoc test were used. Significant strong linear correlations were found between AD γ-metrics and R₂* in both GM and WM of the human brain, but not between DTI-metrics and R₂*. Depending on Δχ driven magnetic field inhomogeneity, the new contrast provided by AD-γ imaging reflects Δχ due to differences in myelin orientation and iron content within selected regions in the WM and GM, respectively. This feature of the AD-γ imaging due to the fact that γ is quantified by using MRI, may be an alternative strategy to investigate, at high magnetic fields, microstructural changes in myelin, and alterations due to iron accumulation. Possible clinical applications might be in the field of neurodegenerative diseases.