Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model

Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data

We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmap Reduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors

PURPOSE

To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

METHODS

We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.

RESULTS

We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.

CONCLUSION

DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.

P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI

Estimating T₂ relaxation time distributions from multi-echo T₂-weighted MRI (T₂W) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating T₂ distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called P₂T₂, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of T₂ distribution estimation. We evaluated our P₂T₂ model in comparison to both DNN-based methods and classical methods for T₂ distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels (SNR<80) which are common in the clinical setting. Further, our model achieved a ∼35% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our P₂T₂ model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our P₂T₂ model offers a reliable and precise means of estimating T₂ distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols. Our source code is available at: https://github.com/Hben-atya/P2T2-Robust-T2-estimation.git.

Span of regularization for solution of inverse problems with application to magnetic resonance relaxometry of the brain

We present a new regularization method for the solution of the Fredholm integral equation (FIE) of the first kind, in which we incorporate solutions corresponding to a range of Tikhonov regularizers into the end result. This method identifies solutions within a much larger function space, spanned by this set of regularized solutions, than is available to conventional regularization methods. An additional key development is the use of dictionary functions derived from noise-corrupted inversion of the discretized FIE. In effect, we combine the stability of solutions with greater degrees of regularization with the resolution of those that are less regularized. The span of regularizations (SpanReg) method may be widely applicable throughout the field of inverse problems.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares

Multi-echo T₂ magnetic resonance images contain information about the distribution of T₂ relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T₂ spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X²) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X², and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T₂ spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T₂ distributions and MWF maps.

Lesion features on magnetic resonance imaging discriminate multiple sclerosis patients

BACKGROUND

Magnetic resonance imaging (MRI) provides insight into various pathological processes in multiple sclerosis (MS) and may provide insight into patterns of damage among patients.

OBJECTIVE

We sought to determine if MRI features have clinical discriminative power among a cohort of MS patients.

METHODS

Ninety-six relapsing remitting and seven progressive MS patients underwent myelin water fraction (MWF) imaging and conventional MRI for cortical thickness and thalamic volume. Patients were clustered based on lesion level MRI features using an agglomerative hierarchical clustering algorithm based on principal component analysis (PCA).

RESULTS

One hundred and three patients with 1689 MS lesions were analyzed. PCA on MRI features demonstrated that lesion MWF and volume distributions (characterized by 25th, 50th, and 75th percentiles) accounted for 87% of the total variability based on four principal components. The best hierarchical cluster confirmed two distinct patient clusters. The clustering features in order of importance were lesion median MWF, MWF 25th, MWF 75th, volume 75th percentiles, median individual lesion volume, total lesion volume, cortical thickness, and thalamic volume (all p values <0.01368). The clusters were associated with patient Expanded Disability Status Scale (EDSS) (n = 103, p = 0.0338) at baseline and at 5 years (n = 72, p = 0.0337).

CONCLUSIONS

These results demonstrate that individual MRI features can identify two patient clusters driven by lesion-based values, and our unique approach is an analysis blinded to clinical variables. The two distinct clusters exhibit MWF differences, most likely representing individual remyelination capabilities among different patient groups. These findings support the concept of patient-specific pathophysiological processes and may guide future therapeutic approaches.

On the use of multicompartment models of diffusion and relaxation for placental imaging

Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications. These models are increasingly making their way into the world of placental imaging. This review provides a framework for their motivation and implementation and describes some of the outstanding questions that need to be answered before they can be routinely adopted.

Myelin content changes in Clinically Isolated Syndrome and Relapsing- Remitting Multiple Sclerosis: Associations with lesion type and severity of visuomotor impairment

BACKGROUND

Cognitive disturbances occur in patients with Relapsing Remitting Multiple Sclerosis (RR-MS) and Clinically Isolated Syndrome (CIS). The Multi-Echo-Spin-Echo (MESE) T2-weighted sequence quantifies demyelination, the pathological hallmark of MS, but has not been used for the documentation of the potential relationship between anatomically specific demyelinating changes and cognitive impairment in MS.

PURPOSE

To identify markers of regional demyelination in patients with RR-MS and CIS in relation to clinical variables and severity of cognitive impairment.

METHODS AND MATERIALS

37 RR-MS patients, 39 CIS patients and 52 healthy controls (HC) were examined using the MESE sequence. Long T2 and myelin water fraction (MWF) values were measured, serving as indices of intra/extracellular water content and myelin content, respectively, in focal white matter lesions and 12 normal appearing white matter (NAWM) areas of the patients and HC. A comprehensive neuropsychological assessment was administered to all patients.

RESULTS

RR-MS patients showed widespread long T2 increases and MWF reductions in NAWM, compared to the respective values of HC (p < 0.001), which correlated with total lesion volume. Among RR-MS patients illness duration correlated negatively with MWF in right hemisphere frontal and periventricular NAWM areas (and positively with corresponding long T2 values). MWF values were lower in the CIS, as compared to the HC group, in the temporal, frontal and periventricular NAWM areas. Focal demyelinating lesions displayed variable higher T2 and lower MWF values, compared to NAWM, closely corresponding to their intensity on T1 sequences. Reduced MWF values and increased long T2 values in right periventricular NAWM were significantly associated with poor visuomotor performance.

CONCLUSION

The MESE sequence affords accurate estimation of myelin and water content in NAWM and focal lesions in RR-MS and CIS patients, by means of the MWF and long T2 values, respectively, providing a sensitive index of demyelination associated with visuomotor deficits.

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Multi-component T₂ relaxometry allows probing tissue microstructure by assessing compartment-specific T₂ relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is the conventional method for estimating smooth T₂ distributions. Despite the improved estimation provided by this method compared to non-regularized NNLS, the solution is still sensitive to the underlying noise and the regularization weight. This is especially relevant for clinically achievable signal-to-noise ratios. In the literature of inverse problems, various well-established approaches to promote smooth solutions, including first-order and second-order Tikhonov regularization, and different criteria for estimating the regularization weight have been proposed, such as L-curve, Generalized Cross-Validation, and Chi-square residual fitting. However, quantitative comparisons between the available reconstruction methods for computing the T₂ distribution, and between different approaches for selecting the optimal regularization weight, are lacking. In this study, we implemented and evaluated ten reconstruction algorithms, resulting from the individual combinations of three penalty terms with three criteria to estimate the regularization weight, plus non-regularized NNLS. Their performance was evaluated both in simulated data and real brain MRI data acquired from healthy volunteers through a scan-rescan repeatability analysis. Our findings demonstrate the need for regularization. As a result of this work, we provide a list of recommendations for selecting the optimal reconstruction algorithms based on the acquired data. Moreover, the implemented methods were packaged in a freely distributed toolbox to promote reproducible research, and to facilitate further research and the use of this promising quantitative technique in clinical practice.

Model-informed machine learning for multi-component T2 relaxometry

Recovering the T₂ distribution from multi-echo T₂ magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T₂ distribution from the signal) approaches to T₂ relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with a priori knowledge of in vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T₂ distribution. We evaluate MIML in comparison to a Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares algorithms (non-parametric) on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than the non-parametric and parametric methods, respectively.

Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation

PURPOSE

To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters.

METHODS

We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T ₁ estimations based on the variable flip angle method.

RESULTS

The proposed method delivers noise-reduced T ₁ parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time.

CONCLUSIONS

This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation.

Multiexponential T2 relaxometry of benign and malignant adipocytic tumours

Background

We investigated a recently proposed multiexponential (Mexp) fitting method applied to T2 relaxometry magnetic resonance imaging (MRI) data of benign and malignant adipocytic tumours and healthy subcutaneous fat. We studied the T2 distributions of the different tissue types and calculated statistical metrics to differentiate benign and malignant tumours.

Methods

Twenty-four patients with primary benign and malignant adipocytic tumours prospectively underwent 1.5-T MRI with a single-slice T2 relaxometry (Carr-Purcell-Meiboom-Gill sequence, 25 echoes) prior to surgical excision and histopathological assessment. The proposed method adaptively chooses a monoexponential or biexponential model on a voxel basis based on the adjusted R² goodness of fit criterion. Linear regression was applied on the statistical metrics derived from the T2 distributions for the classification.

Results

Healthy subcutaneous fat and benign lipoma were better described by biexponential fitting with a monoexponential and biexponential prevalence of 0.0/100% and 0.2/99.8% respectively. Well-differentiated liposarcomas exhibit 17.6% monoexponential and 82.4% biexponential behaviour, while more aggressive liposarcomas show larger degree of monoexponential behaviour. The monoexponential/biexponential prevalence was 47.6/52.4% for myxoid tumours, 52.8/47.2% for poorly differentiated parts of dedifferentiated liposarcomas, and 24.9/75.1% pleomorphic liposarcomas. The percentage monoexponential or biexponential model prevalence per patient was the best classifier distinguishing between malignant and benign adipocytic tumours with a 0.81 sensitivity and a 1.00 specificity.

Conclusions

Healthy adipose tissue and benign lipomas showed a pure biexponential behaviour with similar T2 distributions, while decreased adipocytic cell differentiation characterising aggressive neoplasms was associated with an increased rate of monoexponential decay curves, opening a perspective adipocytic tumour classification.

Impact of Lesion Location on Longitudinal Myelin Water Fraction Change in Chronic Multiple Sclerosis Lesions

BACKGROUND AND PURPOSE

To examine the impact of lesion location on longitudinal myelin water fraction (MWF) changes in chronic multiple sclerosis (MS) lesions. Relative hypoxia, due to vascular watershed regions of the cerebrum, has been implicated in lesion development but impact on ongoing demyelination is unknown.

METHODS

Forty-eight patients with relapsing-remitting and secondary progressive MS had two MWF scans with fast acquisition, spiral trajectory, and T2prep (FAST-T2) sequence, at an interval of 2.0 (±.3) years. Lesion location was identified based upon cerebral lobe and relation to the ventricles. Change in MWF was assessed using a mixed effects model, controlling for lesion location and patient covariates.

RESULTS

Average age was 42.3 (±12) years, mean disease duration was 9.7 (±9.1) years, and median Expanded Disability Status Score (EDSS) was 2.5 (±2.3). The majority of 512 chronic lesions was located in the frontal and parietal lobes (75.6%) and more often periventricular (44.7%). All occipital lesions were periventricular. The average lesion MWF decreased from baseline (.07 ± .03) to 2 years (.06 ±.03) P < .01. Lesions within the occipital lobe showed a significant reduction in MWF as compared to other lobes.

CONCLUSIONS

Chronic lesions in the occipital lobe showed the greatest reduction in MWF. Neuroanatomical localization of lesions to the occipital horns of the lateral ventricles, a watershed region, may contribute to ongoing demyelination in this lesion type.

Multi-exponential Transverse Relaxation Times Estimation from Magnetic Resonance Images under Rician Noise and Spatial Regularization

Relaxation signal inside each voxel of magnetic resonance images (MRI) is commonly fitted by a multi-exponential decay curve. The estimation of a discrete multi-component relaxation model parameters from magnitude MRI data is a challenging nonlinear inverse problem since it should be conducted on the entire image voxels under non-Gaussian noise statistics. This paper proposes an efficient algorithm allowing the joint estimation of relaxation time values and their amplitudes using different criteria taking into account a Rician noise model, combined with a spatial regularization accounting for low spatial variability of relaxation time constants and amplitudes between neighboring voxels. The Rician noise hypothesis is accounted for either by an adapted nonlinear least squares algorithm applied to a corrected least squares criterion or by a majorization-minimization approach applied to the maximum likelihood criterion. In order to solve the resulting large-scale non-negativity constrained optimization problem with a reduced numerical complexity and computing time, an optimization algorithm based on a majorization approach ensuring separability of variables between voxels is proposed. The minimization is carried out iteratively using an adapted Levenberg-Marquardt algorithm that ensures convergence by imposing a sufficient decrease of the objective function and the non-negativity of the parameters. The importance of the regularization alongside the Rician noise incorporation is shown both visually and numerically on a simulated phantom and on magnitude MRI images acquired on fruit samples.

Inverse Laplace transform and multiexponential fitting analysis of T2 relaxometry data: a phantom study with aqueous and fat containing samples

BACKGROUND

The inverse Laplace transform (ILT) is the most widely used method for T2 relaxometry data analysis. This study examines the qualitative agreement of ILT and a proposed multiexponential (Mexp method) regarding the number of T2 components. We performed a feasibility study for the voxelwise characterisation of heterogeneous tissue with T2 relaxometry.

METHODS

Eleven samples of aqueous, fatty and mixed composition were analysed using ILT and Mexp. The phantom was imaged using a 1.5-T system with a single slice T2 relaxometry 25-echo Carr-Purcell-Meiboom-Gill sequence in order to obtain the T2 decay curve with 25 equidistant echo times. The adjusted R² goodness of fit criterion was used to determine the number of T2 components using the Mexp method on a voxel-based analysis. Comparison of mean and standard deviation of T2 values for both methods was performed by fitting a Gaussian function to the ILT resulting vector.

RESULTS

Phantom results showed pure monoexponential decay for acetone and water and pure biexponential behaviour for corn oil, egg yolk, and 35% fat milk cream, while mixtures of egg whites and yolks as well as milk creams with 12-20% fatty composition exhibit mixed monoexponential and biexponential behaviour at different fractions. The number of T2 components by the Mexp method was compared to the ILT-derived spectrum as ground truth.

CONCLUSIONS

Mexp analysis with the adjusted R² criterion can be used for the detection of the T2 distribution of aqueous, fatty and mixed samples with the added advantage of voxelwise mapping.

Myelin water imaging data analysis in less than one minute

PURPOSE

Based on a deep learning neural network (NN) algorithm, a super fast and easy to implement data analysis method was proposed for myelin water imaging (MWI) to calculate the myelin water fraction (MWF).

METHODS

A NN was constructed and trained on MWI data acquired by a 32-echo 3D gradient and spin echo (GRASE) sequence. Ground truth labels were created by regularized non-negative least squares (NNLS) with stimulated echo corrections. Voxel-wise GRASE data from 5 brains (4 healthy, 1 multiple sclerosis (MS)) were used for NN training. The trained NN was tested on 2 healthy brains, 1 MS brain with segmented lesions, 1 healthy spinal cord, and 1 healthy brain acquired from a different scanner.

RESULTS

Production of whole brain MWF maps in approximately 33 ​s can be achieved by a trained NN without graphics card acceleration. For all testing regions, no visual differences between NN and NNLS MWF maps were observed, and no obvious regional biases were found. Quantitatively, all voxels exhibited excellent agreement between NN and NNLS (all R²>0.98, p ​< ​0.001, mean absolute error <0.01).

CONCLUSION

The time for accurate MWF calculation can be dramatically reduced to less than 1 ​min by the proposed NN, addressing one of the barriers facing future clinical feasibility of MWI.

Transverse relaxometry with transmit field-constrained stimulated echo compensation

OBJECTIVE

Purely exponential decay is rarely observed in conventional mono-exponential T₂ mapping due to transmit field inhomogeneity and calibration errors, which collectively introduce stimulated and indirect echo pathways. Stimulated echo correction (SEC) requires an additional fit parameter for the transmit field, resulting in greater uncertainty in T₂ relative to mono-exponential fitting. The aim of this study was to develop an accurate and precise method for T₂ mapping using SEC.

METHODS

The proposed method, called two-step SEC (tSEC), leverages spatial correlations in the transmit field to reduce the number of fully independent fitting parameters from three to two. The method involves a two-pass fit: the first pass involves a fast but standard SEC fit. The initially estimated transmit field is smoothed and provided as a fixed input to the second pass.

RESULTS

Simulations and in vivo experiments demonstrated up to 38% and 27% decreases in relative T₂ variance with tSEC relative to SEC. Average T₂ values were unchanged between tSEC and SEC fits. The proposed method uses the same input data as SEC and exponential fits, so it is applicable to existing data.

DISCUSSION

The proposed method generates reliable and reproducible quantitative T₂ maps and should be considered for future relaxometry studies.

Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry

PURPOSE

Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.

METHODS

PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.

RESULTS

Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.

CONCLUSION

Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Damage to myelin is a key feature of multiple sclerosis (MS) pathology. Magnetic resonance imaging (MRI) has revolutionized our ability to detect and monitor MS pathology in vivo. Proton density, T1 and T2 can provide qualitative contrast weightings that yield superb in vivo visualization of central nervous system tissue and have proved invaluable as diagnostic and patient management tools in MS. However, standard clinical MR methods are not specific to the types of tissue damage they visualize, and they cannot detect subtle abnormalities in tissue that appears otherwise normal on conventional MRIs. Myelin water imaging is an MR method that provides in vivo measurement of myelin. Histological validation work in both human brain and spinal cord tissue demonstrates a strong correlation between myelin water and staining for myelin, validating myelin water as a marker for myelin. Myelin water varies throughout the brain and spinal cord in healthy controls, and shows good intra- and inter-site reproducibility. MS plaques show variably decreased myelin water fraction, with older lesions demonstrating the greatest myelin loss. Longitudinal study of myelin water can provide insights into the dynamics of demyelination and remyelination in plaques. Normal appearing brain and spinal cord tissues show reduced myelin water, an abnormality which becomes progressively more evident over a timescale of years. Diffusely abnormal white matter, which is evident in 20%-25% of MS patients, also shows reduced myelin water both in vivo and postmortem, and appears to originate from a primary lipid abnormality with relative preservation of myelin proteins. Active research is ongoing in the quest to refine our ability to image myelin and its perturbations in MS and other disorders of the myelin sheath.

Use of the NESMA Filter to Improve Myelin Water Fraction Mapping with Brain MRI

BACKGROUND AND PURPOSE

Myelin water fraction (MWF) mapping permits direct visualization of myelination patterns in the developing brain and in pathology. MWF is conventionally measured through multiexponential T₂ analysis which is very sensitive to noise, leading to inaccuracies in derived MWF estimates. Although noise reduction filters may be applied during postprocessing, conventional filtering can introduce bias and obscure small structures and edges. Advanced nonblurring filters, while effective, exhibit a high level of complexity and the requirement for supervised implementation for optimal performance. The purpose of this paper is to demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve the determination of MWF parameter estimates from gradient and spin echo (GRASE) imaging data.

METHODS

We evaluated the performance of the NESMA filter for MWF mapping from clinical GRASE imaging data of the human brain, and compared the results to those calculated from unfiltered images. Numerical and in vivo analyses of the brains of three subjects, representing different ages, were conducted.

RESULTS

Our results demonstrated the potential of the NESMA filter to permit high-quality in vivo MWF mapping. Indeed, NESMA permits substantial reduction of random variation in derived MWF estimates while preserving accuracy and detail.

CONCLUSIONS

In vivo estimation of MWF in the human brain from GRASE imaging data was markedly improved through use of the NESMA filter. The use of NESMA may contribute to the goal of high-quality MWF mapping in clinically feasible imaging times.

Multi-parametric quantitative MRI reveals three different white matter subtypes

INTRODUCTION

Magnetic resonance imaging (MRI) shows slight spatial variations in brain white matter (WM). We used quantitative multi-parametric MRI to evaluate in what respect these inhomogeneities could correspond to WM subtypes with specific characteristics and spatial distribution.

MATERIALS AND METHODS

Twenty-six controls (12 women, 38 ±9 Y) took part in a 60-min session on a 3T scanner measuring 7 parameters: R1 and R2, diffusion tensor imaging which allowed to measure Axial and Radial Diffusivity (AD, RD), magnetization transfer imaging which enabled to compute the Macromolecular Proton Fraction (MPF), and a susceptibility-weighted sequence which permitted to quantify R2* and magnetic susceptibility (χm). Spatial independent component analysis was used to identify WM subtypes with specific combination of quantitative parameters values.

RESULTS

Three subtypes could be identified. t-WM (track) mostly mapped on well-formed projection and commissural tracts and came with high AD values (all p < 10(-18)). The two other subtypes were located in subcortical WM and overlapped with association fibers: f-WM (frontal) was mostly anterior in the frontal lobe whereas c-WM (central) was underneath the central cortex. f-WM and c-WM had higher MPF values, indicating a higher myelin content (all p < 1.7 10(-6)). This was compatible with their larger χm and R2, as iron is essentially stored in oligodendrocytes (all p < 0.01). Although R1 essentially showed the same, its higher value in t-WM relative to c-WM might be related to its higher cholesterol concentration.

CONCLUSIONS

Thus, f- and c-WMs were less structured, but more myelinated and probably more metabolically active regarding to their iron content than WM related to fasciculi (t-WM). As known WM bundles passed though different WM subtypes, myelination might not be uniform along the axons but rather follow a spatially consistent regional variability. Future studies might examine the reproducibility of this decomposition and how development and pathology differently affect each subtype.

Repair strategies for multiple sclerosis: challenges, achievements and perspectives

PURPOSE OF REVIEW

Despite major progress in multiple sclerosis (MS) treatment, to date, accumulation of irreversible clinical disability is not sufficiently prevented with immunotherapies. In this context, repair strategies aimed at reducing axonal damage are becoming a very active field of preclinical and clinical research.

RECENT FINDINGS

Improved understanding of the cellular and molecular mechanisms of myelin repair, together with the emergence of new therapeutic candidates are paving the way for novel therapeutic strategies in MS. In parallel, there is a very active development of imaging methods to assess lesions ongoing remyelination that are crucially needed to evaluate therapeutic efficacy.

SUMMARY

The current development of a very dynamic and multidisciplinary research on remyelination should accelerate the development of myelin repair strategies in MS, to prevent disability progression.

Inferring brain tissue composition and microstructure via MR relaxometry

MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T₁, T₂, and T₂^*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.

Simultaneous Detection of H and D NMR Signals in a Micro-Tesla Field

We present NMR spectra of remote-magnetized deuterated water, detected in an unshielded environment by means of a differential atomic magnetometer. The measurements are performed in a μT field, while pulsed techniques are applied-following the sample displacement-in a 100 μT field, to tip both D and H nuclei by controllable amounts. The broad-band nature of the detection system enables simultaneous detection of the two signals and accurate evaluation of their decay times. The outcomes of the experiment demonstrate the potential of ultra-low-field NMR spectroscopy in important applications where the correlation between proton and deuteron spin-spin relaxation rates as a function of external parameters contains significant information.

Myelin content changes in probable Alzheimer's disease and mild cognitive impairment: Associations with age and severity of neuropsychiatric impairment

BACKGROUND

Existing indices of white matter integrity such as fractional anisotropy and magnetization transfer ratio may not provide optimal specificity to myelin content. In contrast, myelin water fraction (MWF) derived from the multiecho T₂ relaxation time technique may serve as a more direct measure of myelin content.

PURPOSE/HYPOTHESIS

The goal of the present study was to identify markers of regional demyelination in patients with probable Alzheimer's disease (AD) and mild cognitive impairment (MCI) in relation to age and severity of neuropsychiatric impairment.

POPULATION

The sample included patients diagnosed with probable AD (n = 25) or MCI (n = 43), and cognitively intact elderly controls (n = 33).

FIELD STRENGTH/SEQUENCE ASSESSMENT

Long T₂ , short T₂ , and MWF values were measured with a 1.5T scanner in periventricular and deep normal-appearing white matter (NAWM), serving as indices of intra/extracellular water content and myelin content. A comprehensive neuropsychological and neuropsychiatric assessment was administered to all participants.

STATISTICAL TESTS, RESULTS

AD patients displayed higher age-adjusted long and short T₂ values and reduced MWF values in left temporal/parietal and bilateral periventricular NAWM than controls and MCI patients (P < 0.004; one-way analysis of covariance [ANCOVA] tests). Short T₂ /MWF values in temporal, frontal, and periventricular NAWM of controls and/or MCI patients were significantly associated with episodic and semantic memory performance and depressive symptomatology (P < 0.004; partial correlation indices). The impact of age on memory performance was significantly (P < 0.01; mediated linear regression analyses) mediated by age-related changes in short T₂ and MWF values in these regions.

DATA CONCLUSION

Age-related demyelination is associated with memory impairment (especially in prodromal dementia states) and symptoms of depression in an anatomically specific manner.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1359-1372.

Obtaining sparse distributions in 2D inverse problems

The mathematics of inverse problems has relevance across numerous estimation problems in science and engineering. L₁ regularization has attracted recent attention in reconstructing the system properties in the case of sparse inverse problems; i.e., when the true property sought is not adequately described by a continuous distribution, in particular in Compressed Sensing image reconstruction. In this work, we focus on the application of L₁ regularization to a class of inverse problems; relaxation-relaxation, T₁-T₂, and diffusion-relaxation, D-T₂, correlation experiments in NMR, which have found widespread applications in a number of areas including probing surface interactions in catalysis and characterizing fluid composition and pore structures in rocks. We introduce a robust algorithm for solving the L₁ regularization problem and provide a guide to implementing it, including the choice of the amount of regularization used and the assignment of error estimates. We then show experimentally that L₁ regularization has significant advantages over both the Non-Negative Least Squares (NNLS) algorithm and Tikhonov regularization. It is shown that the L₁ regularization algorithm stably recovers a distribution at a signal to noise ratio<20 and that it resolves relaxation time constants and diffusion coefficients differing by as little as 10%. The enhanced resolving capability is used to measure the inter and intra particle concentrations of a mixture of hexane and dodecane present within porous silica beads immersed within a bulk liquid phase; neither NNLS nor Tikhonov regularization are able to provide this resolution. This experimental study shows that the approach enables discrimination between different chemical species when direct spectroscopic discrimination is impossible, and hence measurement of chemical composition within porous media, such as catalysts or rocks, is possible while still being stable to high levels of noise.

MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning

A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (135 and 23 patients, respectively). It was compared to an approach based on lesion masks. The gamma mixture distribution of whole brain, white matter (WM) MWF was characterized with three variables: the mode (most frequent value) m₁ of the gamma component shown to relate to lesion, the mode m₂ of the component shown to be associated with normal appearing (NA) WM, and the mixing ratio (λ) between the two distributions. The lesion-mask approach relied on the mean MWF within lesion and within NAWM. A multivariate regression analysis was carried out to find the best predictors of cortical thickness for each group and for each approach. The gamma-mixture method was shown to outperform the lesion-mask approach in terms of adjusted R², both for the RRMS and SPMS groups. The predictors of the final gamma-mixture models were found to be m₁ (β = 1.56, p < 0.005), λ (β = −0.30, p < 0.0005) and age (β = −0.0031, p < 0.005) for the RRMS group (adjusted R² = 0.16), and m₂ (β = 4.72, p < 0.0005) for the SPMS group (adjusted R² = 0.45). Further, a DICE coefficient analysis demonstrated that the lesion mask had more overlap to an ROI associated with m₁, than to an ROI associated with m₂ (p < 0.00001), and vice versa for the NAWM mask (p < 0.00001). These results suggest that during the relapsing phase, focal WM damage is associated with cortical thinning, yet in SPMS patients, global WM deterioration has a much stronger influence on secondary degeneration. Through these findings, we demonstrate the potential contribution of myelin loss on neuronal degeneration at different disease stages and the usefulness of our statistical reduction technique which is not affected by the typical bias associated with approaches based on lesion masks.

Magnetic Resonance of Myelin Water: An in vivo Marker for Myelin

Myelin is critical for healthy brain function. An accurate in vivo measure of myelin content has important implications for understanding brain plasticity and neurodegenerative diseases. Myelin water imaging is a magnetic resonance imaging method which can be used to visualize myelination in the brain and spinal cord in vivo. This review presents an overview of myelin water imaging data acquisition and analysis, post-mortem validation work, findings in both animal and human studies and a brief discussion about other MR techniques purported to provide in vivo myelin content. Multi-echo T₂ relaxation approaches continue to undergo development and whole-brain imaging time now takes less than 10 minutes; the standard analysis method for this type of data acquisition is a non-negative least squares approach. Alternate methods including the multi-flip angle gradient echo mcDESPOT are also being used for myelin water imaging. Histological validation studies in animal and human brain and spinal cord tissue demonstrate high specificity of myelin water imaging for myelin. Potential confounding factors for in vivo myelin water fraction measurement include the presence of myelin debris and magnetization exchange processes. Myelin water imaging has successfully been used to study animal models of injury, applied in healthy human controls and can be used to assess damage and injury in conditions such as multiple sclerosis, neuromyelitis optica, schizophrenia, phenylketonuria, neurofibromatosis, niemann pick’s disease, stroke and concussion. Other quantitative magnetic resonance approaches that are sensitive to, but not specific for, myelin exist including magnetization transfer, diffusion tensor imaging and T₁ weighted imaging.

A New Strategy for Fast MRI-Based Quantification of the Myelin Water Fraction: Application to Brain Imaging in Infants

The volume fraction of water related to myelin (fmy) is a promising MRI index for in vivo assessment of brain myelination, that can be derived from multi-component analysis of T1 and T2 relaxometry signals. However, existing quantification methods require rather long acquisition and/or post-processing times, making implementation difficult both in research studies on healthy unsedated children and in clinical examinations. The goal of this work was to propose a novel strategy for fmy quantification within acceptable acquisition and post-processing times. Our approach is based on a 3-compartment model (myelin-related water, intra/extra-cellular water and unrestricted water), and uses calibrated values of inherent relaxation times (T1c and T2c) for each compartment c. Calibration was first performed on adult relaxometry datasets (N = 3) acquired with large numbers of inversion times (TI) and echo times (TE), using an original combination of a region contraction approach and a non-negative least-square (NNLS) algorithm. This strategy was compared with voxel-wise fitting, and showed robust estimation of T1c and T2c. The accuracy of fmy calculations depending on multiple factors was investigated using simulated data. In the testing stage, our strategy enabled fast fmy mapping, based on relaxometry datasets acquired with reduced TI and TE numbers (acquisition <6 min), and analyzed with NNLS algorithm (post-processing <5min). In adults (N = 13, mean age 22.4±1.6 years), fmy maps showed variability across white matter regions, in agreement with previous studies. In healthy infants (N = 18, aged 3 to 34 weeks), asynchronous changes in fmy values were demonstrated across bundles, confirming the well-known progression of myelination.

Rapid simultaneous high-resolution mapping of myelin water fraction and relaxation times in human brain using BMC-mcDESPOT

A number of central nervous system (CNS) diseases exhibit changes in myelin content and magnetic resonance longitudinal, T₁, and transverse, T₂, relaxation times, which therefore represent important biomarkers of CNS pathology. Among the methods applied for measurement of myelin water fraction (MWF) and relaxation times, the multicomponent driven equilibrium single pulse observation of T₁ and T₂ (mcDESPOT) approach is of particular interest. mcDESPOT permits whole brain mapping of multicomponent T₁ and T₂, with data acquisition accomplished within a clinically realistic acquisition time. Unfortunately, previous studies have indicated the limited performance of mcDESPOT in the setting of the modest signal-to-noise range of high-resolution mapping, required for the depiction of small structures and to reduce partial volume effects. Recently, we showed that a new Bayesian Monte Carlo (BMC) analysis substantially improved determination of MWF from mcDESPOT imaging data. However, our previous study was limited in that it did not discuss determination of relaxation times. Here, we extend the BMC analysis to the simultaneous determination of whole-brain MWF and relaxation times using the two-component mcDESPOT signal model. Simulation analyses and in-vivo human brain studies indicate the overall greater performance of this approach compared to the stochastic region contraction (SRC) algorithm, conventionally used to derive parameter estimates from mcDESPOT data. SRC estimates of the transverse relaxation time of the long T₂ fraction, T_2,l, and the longitudinal relaxation time of the short T₁ fraction, T_1,s, clustered towards the lower and upper parameter search space limits, respectively, indicating failure of the fitting procedure. We demonstrate that this effect is absent in the BMC analysis. Our results also showed improved parameter estimation for BMC as compared to SRC for high-resolution mapping. Overall we find that the combination of BMC analysis and mcDESPOT, BMC-mcDESPOT, shows excellent performance for accurate high-resolution whole-brain mapping of MWF and bi-component transverse and longitudinal relaxation times within a clinically realistic acquisition time.

Imaging Renal Urea Handling in Rats at Millimeter Resolution using Hyperpolarized Magnetic Resonance Relaxometry

In vivo spin spin relaxation time (T₂) heterogeneity of hyperpolarized [¹³C,¹⁵N₂]urea in the rat kidney was investigated. Selective quenching of the vascular hyperpolarized ¹³C signal with a macromolecular relaxation agent revealed that a long-T₂ component of the [¹³C,¹⁵N₂]urea signal originated from the renal extravascular space, thus allowing the vascular and renal filtrate contrast agent pools of the [¹³C,¹⁵N₂]urea to be distinguished via multi-exponential analysis. The T₂ response to induced diuresis and antidiuresis was performed with two imaging agents: hyperpolarized [¹³C,¹⁵N₂]urea and a control agent hyperpolarized bis-1,1-(hydroxymethyl)-1-¹³C-cyclopropane-²H₈. Large T₂ increases in the inner-medullar and papilla were observed with the former agent and not the latter during antidiuresis. Therefore, [¹³C,¹⁵N₂]urea relaxometry is sensitive to two steps of the renal urea handling process: glomerular filtration and the inner-medullary urea transporter (UT)-A1 and UT-A3 mediated urea concentrating process. Simple motion correction and subspace denoising algorithms are presented to aid in the multi exponential data analysis. Furthermore, a T₂-edited, ultra long echo time sequence was developed for sub-2 mm³ resolution 3D encoding of urea by exploiting relaxation differences in the vascular and filtrate pools.

Imaging Renal Urea Handling in Rats at Millimeter Resolution using Hyperpolarized Magnetic Resonance Relaxometry

In vivo spin spin relaxation time (T₂) heterogeneity of hyperpolarized [¹³C,¹⁵N₂]urea in the rat kidney was investigated. Selective quenching of the vascular hyperpolarized ¹³C signal with a macromolecular relaxation agent revealed that a long-T₂ component of the [¹³C,¹⁵N₂]urea signal originated from the renal extravascular space, thus allowing the vascular and renal filtrate contrast agent pools of the [¹³C,¹⁵N₂]urea to be distinguished via multi-exponential analysis. The T₂ response to induced diuresis and antidiuresis was performed with two imaging agents: hyperpolarized [¹³C,¹⁵N₂]urea and a control agent hyperpolarized bis-1,1-(hydroxymethyl)-1-¹³C-cyclopropane-²H₈. Large T₂ increases in the inner-medullar and papilla were observed with the former agent and not the latter during antidiuresis. Therefore, [¹³C,¹⁵N₂]urea relaxometry is sensitive to two steps of the renal urea handling process: glomerular filtration and the inner-medullary urea transporter (UT)-A1 and UT-A3 mediated urea concentrating process. Simple motion correction and subspace denoising algorithms are presented to aid in the multi exponential data analysis. Furthermore, a T₂-edited, ultra long echo time sequence was developed for sub-2 mm³ resolution 3D encoding of urea by exploiting relaxation differences in the vascular and filtrate pools.

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS.

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS.

T2 relaxometry in the extremely-preterm brain at adolescence

Survival following very preterm birth is associated with cognitive and behavioral sequelae, which may have identifiable neural correlates. Many survivors of modern neonatal care in the 1990s are now young adults and the evolution of MRI findings into adult life has rarely been evaluated. We have investigated a cohort of 19-year-old adolescents without severe impairments born between 22 and 26 weeks of gestation in 1995 (extremely preterm: EP). Using T2 data derived from magnetic resonance imaging we investigate differences between the brains of 46 EP participants (n = 46) and the brains of a group of term-born controls (n = 20). Despite EP adolescents having significantly reduced gray and white matter volumes, the composition of these tissues, assessed by both single and multi-component relaxometry, appears to be unrelated to either preterm status or gender. This may represent either insensitivity of the imaging technique or reflect that there are only subtle differences between EP subjects and their term-born peers.

Profilometry: A new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis

AIMS

describe a new "profilometry" framework for the multimetric analysis of white matter tracts, and demonstrate its application to multiple sclerosis (MS) with radial diffusivity (RD) and myelin water fraction (MWF).

METHODS

A cohort of 15 normal controls (NC) and 141 MS patients were imaged with T1, T2 FLAIR, T2 relaxometry and diffusion MRI (dMRI) sequences. T1 and T2 FLAIR allowed for the identification of patients having lesion(s) on the tracts studied, with a special focus on the forceps minor. T2 relaxometry provided MWF maps, while dMRI data yielded RD maps and the tractography required to compute MWF and RD tract profiles. The statistical framework combined a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections.

RESULTS

In the single-case case study the profilometry visualization showed a clear departure of MWF and RD from the NC normative data at the lesion location(s). Group comparison from MANCOVA demonstrated significant differences at lesion locations, and a significant age effect in several tracts. The follow-up LDA analysis suggested MWF better discriminates groups than RD.

DISCUSSION AND CONCLUSION

While progress has been made in both tract-profiling and metrics for white matter characterization, no single framework for a joint analysis of multimodality tract profiles accounting for age and gender is known to exist. The profilometry analysis and visualization appears to be a promising method to compare groups using a single score from MANCOVA while assessing the contribution of each metric with LDA.

Measuring longitudinal myelin water fraction in new multiple sclerosis lesions

Objectives

Investigating the potential of myelin repair strategies in multiple sclerosis (MS) requires an understanding of myelin dynamics during lesion evolution. The objective of this study is to longitudinally measure myelin water fraction (MWF), an MRI biomarker of myelin, in new MS lesions and to identify factors that influence their subsequent myelin content.

Methods

Twenty-three MS patients were scanned with whole-brain Fast Acquisition with Spiral Trajectory and T2prep (FAST-T2) MWF mapping at baseline and median follow-up of 6 months. Eleven healthy controls (HC) confirmed the reproducibility of FAST-T2 in white matter regions of interests (ROIs) similar to a lesion size. A random-effect-model was implemented to determine the association between baseline clinical and lesion variables and the subsequent MWF.

Results

ROI-based measurements in HCs were highly correlated between scans [mean r = 0.893 (.764–.967)]. In MS patients, 38 gadolinium enhancing (Gd+) and 25 new non-enhancing (Gd−) T2 hyperintense lesions (5.7 months, ±3.8) were identified. Significant improvement in MWF was seen in Gd+ lesions (0.035 ± 0.029, p < 0.001) as compared to Gd− lesions (0.006 ± 0.017, p = 0.065). In the model, a higher baseline MWF (p < 0.001) and the presence of Gd (p < 0.001) were associated with higher subsequent MWF.

Conclusions

FAST T2 provides a clinically feasible method to quantify MWF in new MS lesions. The observed influence of baseline MWF, which represents a combined effect of both resolving edema and myelin change within acute lesions, suggests that the extent of initial inflammation impacts final myelin recovery.

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FAST-T2 can measure the extent of myelin loss within early MS lesions.

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The largest study utilizing an in-vivo MRI method to assess MS lesion change

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The majority of change occurs in the earliest stages after MS lesion development.

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The intensity of the acute inflammatory event is detrimental on MWF recovery.

Physiological noise compensation in gradient-echo myelin water imaging

In MRI, physiological noise which originates from cardiac and respiratory functions can induce substantial errors in detecting small signals in the brain. In this work, we explored the effects of the physiological noise and their compensation methods in gradient-echo myelin water imaging (GRE-MWI). To reduce the cardiac function induced inflow noise, flow saturation RF pulses were applied to the inferior portion of the head, saturating inflow blood signals. For the respiratory function induced B0 fluctuation compensation, a navigator echo was acquired, and respiration induced phase errors were corrected during reconstruction. After the compensations, the resulting myelin water images show substantially improved image quality and reproducibility. These improvements confirm the importance and usefulness of the physiological noise compensations in GRE-MWI.