Myelin Water Fraction Mapping Research Articles

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.

Temperature dependence and histological correlation of inhomogeneous magnetization transfer and myelin water imaging in ex vivo brain

PurposeThe promise of inhomogeneous magnetization transfer (ihMT) as a new myelin imaging method was studied in ex vivo human brain tissue and in relation to myelin water fraction (MWF). The temperature dependence of both methods was characterized, as well as their correspondence with a histological measure of myelin content. Unfiltered and filtered ihMT protocols were studied by adjusting the saturation scheme to preserve or attenuate signal from tissue with short dipolar relaxation time T1D. MethodsihMT ratio (ihMTR) and MWF maps were acquired at 7 T from formalin-fixed human brain samples at 22.5 °C, 30 °C and 37 °C. The impact of temperature on unfiltered ihMTR, filtered ihMTR and MWF was investigated and compared to myelin basic protein staining. ResultsUnfiltered ihMTR exhibited no temperature dependence, whereas filtered ihMTR increased with increasing temperature. MWF decreased at higher temperature, with an increasing prevalence of areas where the myelin water signal was unreliably determined, likely related to a reduction in T2 peak separability at higher temperatures ex vivo. MWF and ihMTR showed similar per-sample correlation with myelin staining at room temperature. At 37 °C, filtered ihMTR was more strongly correlated with myelin staining and had increased dynamic range compared to unfiltered ihMTR. ConclusionsGiven the temperature dependence of filtered ihMT, increased dynamic range, and strong myelin specificity that persists at higher temperatures, we recommend carefully controlled temperatures close to 37 °C for filtered ihMT acquisitions. Unfiltered ihMT may also be useful, due to its independence from temperature, higher amplitude values, and sensitivity to short T1D components. Ex vivo myelin water imaging should be performed at room temperature, to avoid fitting issues found at higher temperatures.

Variable flip angle echo planar time-resolved imaging (vFA-EPTI) for fast high-resolution gradient echo myelin water imaging

Myelin water imaging techniques based on multi-compartment relaxometry have been developed as an important tool to measure myelin concentration in vivo, but are limited by the long scan time of multi-contrast multi-echo acquisition. In this work, a fast imaging technique, termed variable flip angle Echo Planar Time-Resolved Imaging (vFA-EPTI), is developed to acquire multi-echo and multi-flip-angle gradient-echo data with significantly reduced acquisition time, providing rich information for multi-compartment analysis of gradient-echo myelin water imaging (GRE-MWI). The proposed vFA-EPTI method achieved 26 folds acceleration with good accuracy by utilizing an efficient continuous readout, optimized spatiotemporal encoding across echoes and flip angles, as well as a joint subspace reconstruction. An approach to estimate off-resonance field changes between different flip-angle acquisitions was also developed to ensure high-quality joint reconstruction across flip angles. The accuracy of myelin water fraction (MWF) estimate under high acceleration was first validated by a retrospective undersampling experiment using a lengthy fully-sampled data as reference. Prospective experiments were then performed where whole-brain MWF and multi-compartment quantitative maps were obtained in 5 min at 1.5 mm isotropic resolution and 24 min at 1 mm isotropic resolution at 3T. Additionally, ultra-high resolution data at 600 µm isotropic resolution were acquired at 7T, which show detailed structures within the cortex such as the line of Gennari, demonstrating the ability of the proposed method for submillimeter GRE-MWI that can be used to study cortical myeloarchitecture in vivo.

Fast and high-resolution myelin water imaging: Accelerating multi-echo GRASE with CAIPIRINHA.

Although several MRI methods have been explored to achieve in vivo myelin quantification, imaging the whole brain in clinically acceptable times and sufficiently high resolution remains challenging. To address this problem, this work investigates the acceleration of multi-echo T2 acquisitions based on the multi-echo gradient and spin echo (GRASE) sequence using CAIPIRINHA undersampling and adapted k-space reordering patterns. A prototype multi-echo GRASE sequence supporting CAIPIRINHA parallel imaging was implemented. Multi-echo T2 data were acquired from 12 volunteers using the implemented sequence (1.6 × 1.6 × 1.6 mm3 , 84 slices, acquisition time [TA] = 10:30 min) and a multi-echo spin echo (MESE) sequence as reference (1.6 × 1.6 × 3.2 mm3 , single-slice, TA = 5:41 min). Myelin water fraction (MWF) maps derived from both acquisitions were compared via correlation and Bland-Altman analyses. In addition, scan-rescan datasets were acquired to evaluate the repeatability of the derived maps. Resulting maps from the MESE and multi-echo GRASE sequences were found to be correlated (r = 0.83). The Bland-Altman analysis revealed a mean bias of -0.2% (P = .24) with the limits of agreement ranging from -3.7% to 3.3%. The Pearson's correlation coefficient among MWF values obtained from the scan-rescan datasets was found to be 0.95 and the mean bias equal to 0.11% (P = .32), indicating good repeatability of the retrieved maps. By combining a 3D multi-echo GRASE sequence with CAIPIRINHA sampling, whole-brain MWF maps were obtained in 10:30 min with 1.6 mm isotropic resolution. The good correlation with conventional MESE-based maps demonstrates that the implemented sequence may be a promising alternative to time-consuming MESE acquisitions.

Artificial neural network for multi-echo gradient echo-based myelin water fraction estimation.

To demonstrate robust myelin water fraction (MWF) mapping using an artificial neural network (ANN) with multi-echo gradient-echo (GRE) signal. Multi-echo gradient-echo signals simulated with a three-pool exponential model were used to generate the training data set for the ANN, which was designed to yield the MWF. We investigated the performance of our proposed ANN for various conditions using both numerical simulations and in vivo data. Simulations were conducted with various SNRs to investigate the performance of the ANN. In vivo data with high spatial resolutions were applied in the analyses, and results were compared with MWFs derived by the nonlinear least-squares algorithm using a complex three-pool exponential model. The network results for the simulations show high accuracies against noise compared with nonlinear least-squares MWFs: RMS-error value of 5.46 for the nonlinear least-squares MWF and 3.56 for the ANN MWF at an SNR of 150 (relative gain=34.80%). These effects were also found in the in vivo data, with reduced SDs in the region-of-interest analyses. These effects of the ANN demonstrate the feasibility of acquiring high-resolution myelin water images. The simulation results and in vivo data suggest that the ANN facilitates more robust MWF mapping in multi-echo gradient-echo sequences compared with the conventional nonlinear least-squares method.

Multi-compartment relaxometry and diffusion informed myelin water imaging – Promises and challenges of new gradient echo myelin water imaging methods

Myelin water fraction (MWF) mapping based on data fitting of a 3-pool exponential model of multi-echo gradient echo (mGRE) data using MRI shows great promises for in vivo myelin quantification. However, this multi-exponential fitting is ill-conditioned because of the similar relaxation times and frequency shifts of the various compartments. Additionally, the bound water residing in the myelin sheath of white matter is expected to have a faster longitudinal magnetisation recovery than that of the free water in the intra-axonal and extra-axonal space. When the Ernst angle is used to achieve maximum SNR and improve fitting, this will introduce a T1-weighting effect to the derived MWF. In this study, we first demonstrate that diffusion-weighted imaging can be used to infer the compartmental signal properties using an analytical fibre model to achieve a robust MWF estimation. Second, we show that by incorporating a variable flip angle scheme to the mGRE acquisition with a multi-compartment relaxometry model, not only the MWF is corrected from the T1 dependency but also the fitting procedure becomes less ill-conditioned and more SNR efficient. Finally, we demonstrate these two approaches can be combined to allow higher spatial resolution MWF maps than what has been reported to date with robust MWF estimation on a small cohort.

Myelin water fraction estimation using small-tip fast recovery MRI.

To demonstrate the feasibility of an optimized set of small-tip fast recovery (STFR) MRI scans for rapidly estimating myelin water fraction (MWF) in the brain. We optimized a set of STFR scans to minimize the Cramér-Rao Lower Bound of MWF estimates. We evaluated the RMSE of MWF estimates from the optimized scans in simulation. We compared STFR-based MWF estimates (both modeling exchange and notmodeling exchange) to multi-echo spin echo (MESE)-based estimates. We used the optimized scans to acquire in vivo data from which a MWF map was estimated. We computed the STFR-based MWF estimates using PERK, a recently developed kernel regression technique, and the MESE-based MWF estimates using both regularized non-negative least squares (NNLS) and PERK. In simulation, the optimized STFR scans led to estimates of MWF with low RMSE across a range of tissue parameters and across white matter and gray matter. The STFR-based MWF estimates that modeled exchange compared well to MESE-based MWF estimates in simulation. When the optimized scans were tested in vivo, the MWF map that was estimated using a 3-compartment model with exchange was closer to the MESE-based MWF map. The optimized STFR scans appear to be well suited for estimating MWF in simulation and in vivo when we model exchange in training. In this case, the STFR-based MWF estimates are close to the MESE-based estimates.

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2 decay data.

Multi-compartment T2 mapping has gained particular relevance for the study of myelin water in the brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non-negative least squares fitting of multi-echo T2 data has been widely employed for the computation of the myelin water fraction (MWF), and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B1+ homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi-compartment T2 and MWF maps. We generated multi-echo spin-echo T2 decay curves following the Carr-Purcell-Meiboom-Gill approach for various myelin concentrations and myelin T2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra-/extracellular water geometric mean T2 (gmT2 ). MWF estimates were more stable than myelin water gmT2 time across different settings of the T2 analysis. We observed that the lower limit of the T2 distribution grid should be slightly shorter than TE1 . Both TE1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T2 signal. Among all parameters of interest, the estimated MWF and intra-/extracellular water gmT2 differed by approximately 0.13-4 percentage points and 3-4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B1+ inhomogeneities and at lower signal-to-noise ratio. Tailoring acquisition strategies may allow us to better characterize the T2 distribution, including the myelin water, in vivo.

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.

Myelin water imaging data analysis in less than one minute

PurposeBased 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). MethodsA 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. ResultsProduction 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). ConclusionThe 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.

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging.

In conventional gradient-echo myelin water imaging (GRE-MWI), myelin water fraction (MWF) is estimated by fitting the multi-echo gradient recalled echo (mGRE) signal to a pre-assumed numerical model (e.g., multi-component exponential curves or three component exponential curves). However, in mGRE, imaging artifacts (e.g., voxel spread function and physiological noise) and noise render the signal to deviate from the numerical model, leading to misfit of the model parameters. Here, as an alternative to the model-based GRE-MWI, a blind source separation (BSS) technique for the separation of multi-exponential mGRE signal is proposed. Among the various BSS techniques, a modified robust principal component analysis (rPCA) is presented to separate signal sources by enforcing the data-driven properties such as "low rankness" and "sparsity." Considering the signal evolution of T2∗ relaxation (i.e., non-negative exponential decay), low rankness of exponential decay was enforced by nonnegative matrix factorization (NMF) and hankelization. This method provides the separation of slow-decaying, fast-decaying exponential components and artifact components from mGRE images. After the separation, MWF map is reconstructed as the ratio of the fast-decaying component to the total decaying components. The proposed method was demonstrated in numerical simulations and in vivo scans. The method provided a robust estimation of MWF in the presence of statistical noise and imaging artifacts.

Assessment and correction of macroscopic field variations in 2D spoiled gradient-echo sequences.

PurposeTo model and correct the dephasing effects in the gradient‐echo signal for arbitrary RF excitation pulses with large flip angles in the presence of macroscopic field variations.MethodsThe dephasing of the spoiled 2D gradient‐echo signal was modeled using a numerical solution of the Bloch equations to calculate the magnitude and phase of the transverse magnetization across the slice profile. Additionally, regional variations of the transmit RF field and slice profile scaling due to macroscopic field gradients were included. Simulations, phantom, and in vivo measurements at 3 T were conducted for R2∗ and myelin water fraction (MWF) mapping.ResultsThe influence of macroscopic field gradients on R2∗ and myelin water fraction estimation can be substantially reduced by applying the proposed model. Moreover, it was shown that the dephasing over time for flip angles of 60° or greater also depends on the polarity of the slice‐selection gradient because of phase variation along the slice profile.ConclusionSubstantial improvements in R2∗ accuracy and myelin water fraction mapping coverage can be achieved using the proposed model if higher flip angles are required. In this context, we demonstrated that the phase along the slice profile and the polarity of the slice‐selection gradient are essential for proper modeling of the gradient‐echo signal in the presence of macroscopic field variations.

Inherent spatial structure in myelin water fraction maps

Myelin water fraction (MWF) images in brain tend to be spatially noisy with unknown or no apparent spatial patterns structure, so values are therefore typically averaged over large white matter (WM) volumes. We investigated the existence of an inherent spatial structure in MWF maps and explored the benefits of examining MWF values along diffusion tensor imaging (DTI)-derived white matter tracts. We compared spatial anisotropy between MWF and the more widely-used fractional anisotropy (FA) measure. Sixteen major white matter fibre bundles were extracted based on DTI data from 41 healthy subjects. MWF coefficients of variation (CoV) were computed in sub-segments along each fibre tract and compared to MWF CoVs from the surrounding “tubes” – i.e. voxels just exterior to the tract – of each segment. We further assessed the consistency of the MWF along fibre bundles across subjects and investigated the benefit of examining MWF values in sections along each fibre bundle rather than integrating over the whole tract. CoVs of MWF and FA were lower in fibre bundles compared to their enclosing tubes in all investigated tracts. Both measures possessed a spatial gradient of CoV that was smaller aligned along, compared to perpendicular to, the fibre bundles. All WM tracts showed MWF profiles along their trajectory that were consistent across subjects and were more accurate than the mean overall fibre MWF value in estimating ages of the subjects. We conclude that, although less obvious visually, the spatial MWF distribution in white matter consistently follows a distinct pattern along underlying fibre bundles across subjects. Assessing MWF in sections along white matter tracts may provide a sensitive and robust way to assess myelin across subjects.

Echo time-range effects on gradient-echo based myelin water fraction mapping at 3T.

To investigate the effect of the range of TE on multi-echo gradient echo-based myelin water fraction (MWF) mapping at 3 T. Myelin water fraction estimation was performed using 3 widely used models: magnitude multipool, magnitude 3-pool, and complex 3-pool model. Simulations were conducted using a hollow cylinder model with susceptibility anisotropy. The TE range was varied, and its effect on MWF estimation was observed. The same analysis was also performed with in vivo data from 4 healthy volunteers. Different MWFs were obtained depending on the range of TE data acquired. In the simulations, the results of both magnitude models had a bias with respect to the true values: a maximum bias of up to 20% for the multipool model, and a maximum bias of up to 8% for the 3-pool model. This bias was reduced using the complex 3-pool model (maximum bias was under 2%). These effects were also found in the in vivo data. The MWF values using the complex model was most stable with respect to the TE range. The results of the simulation and in vivo data suggest that MWF values fitted using magnitude models are more influenced by the TE range collected for fitting than using the complex model. The complex model is helpful in mitigating bias due to dependencies over the TE range selection.

Myelin water fraction changes in febrile seizures

ObjectiveThe objective of this feasibility study was to investigate whether myelin water fraction (MWF) patterns can differentiate children presenting with febrile seizures who will go on to develop nonfebrile epilepsy from those who will not. Patients and methodsAs part of a prospective study of myelination patterns in pediatric epilepsy, seven subjects with febrile seizures underwent magnetic resonance imaging (MRI) including the following standard sequences—T1-weighted, T2-weighted, fluid-attenuated inversion recovery (FLAIR)—and an additional experimental sequence, multicomponent-derived equilibrium single-pulse observation of T1 and T2 (mcDESPOT) to quantify MWF. For each of these subjects, MWF maps were derived and compared with an age-matched population-averaged MWF atlas. ResultsAll seven subjects (<5 years old) initially presented with febrile seizures. Of the seven, four had complex seizures and three had simple seizures. All of the children with simple febrile seizures had higher MWF compared with model-derived controls and did not develop epilepsy. All of the children with complex febrile seizures had lower MWF than their model-derived control, and two of these subjects later developed epilepsy. ConclusionThis is the first study in which MWF maps were used to study children with febrile *seizures. This data suggests that relatively higher or stable MWF compared with normative data indicates a lower risk of nonfebrile epilepsy while relatively lower MWF may indicate a pathological condition that could lead to nonfebrile epilepsy.

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

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. 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. 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. 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.

Combining Quantitative Susceptibility Mapping with Automatic Zero Reference (QSM0) and Myelin Water Fraction Imaging to Quantify Iron-Related Myelin Damage in Chronic Active MS Lesions.

A hyperintense rim on susceptibility in chronic MS lesions is consistent with iron deposition, and the purpose of this study was to quantify iron-related myelin damage within these lesions as compared with those without rim. Forty-six patients had 2 longitudinal quantitative susceptibility mapping with automatic zero reference scans with a mean interval of 28.9 ± 11.4 months. Myelin water fraction mapping by using fast acquisition with spiral trajectory and T2 prep was obtained at the second time point to measure myelin damage. Mixed-effects models were used to assess lesion quantitative susceptibility mapping and myelin water fraction values. Quantitative susceptibility mapping scans were on average 6.8 parts per billion higher in 116 rim-positive lesions compared with 441 rim-negative lesions (P < .001). All rim-positive lesions retained a hyperintense rim over time, with increasing quantitative susceptibility mapping values of both the rim and core regions (P < .001). Quantitative susceptibility mapping scans and myelin water fraction in rim-positive lesions decreased from rim to core, which is consistent with rim iron deposition. Whole lesion myelin water fractions for rim-positive and rim-negative lesions were 0.055 ± 0.07 and 0.066 ± 0.04, respectively. In the mixed-effects model, rim-positive lesions had on average 0.01 lower myelin water fraction compared with rim-negative lesions (P < .001). The volume of the rim at the initial quantitative susceptibility mapping scan was negatively associated with follow-up myelin water fraction (P < .01). Quantitative susceptibility mapping rim-positive lesions maintained a hyperintense rim, increased in susceptibility, and had more myelin damage compared with rim-negative lesions. Our results are consistent with the identification of chronic active MS lesions and may provide a target for therapeutic interventions to reduce myelin damage.

Improved three-dimensional multi-echo gradient echo based myelin water fraction mapping with phase related artifact correction

Myelin water fraction (MWF) mapping with a multi-echo gradient echo (mGRE) sequence using complex-value based model fitting approach was recently described, in which the phase of the data plays an important role in the accuracy of the fitting results. Thus, influences coming from the undesirable phase components need to be reduced. Targeted for improved MWF mapping, methods to combat these phase related issues which include offset correction, main magnetic field (B0) inhomogeneity related correction, and flow compensation have been developed. For offset correction, a coil combine method with bipolar readout gradients has been devised. For B0 related artifact, a voxel spread function correction approach along with a navigator echo acquisition was utilized. Finally, flow compensation gradients were used to reduce the effects of flow from regions including the veins. The correction methods led to reduced residual fitting error and improved quality of the resultant MWF maps. Quantitative analysis shows increased reliability when the corrections were used. Data from normal volunteers showed improved MWF mapping with the integrated method developed in this work.

High resolution myelin water imaging incorporating local tissue susceptibility analysis

Quantitative myelin water imaging (MWI) from signal T2* decay acquired with multiple Gradient-Recalled Echo (mGRE) sequence has been widely used since its first report. A recent study showed that with low resolution data (2mm isotropic voxels), direct application of complex fitting to a three-pool WM model with frequency shift terms could produce more stable parameter estimation for myelin water fraction mapping. MWI maps of higher spatial resolution resulting in more detailed tissue structures and reduced partial volume effects around white matter/gray matter (WM/GM) interface, however, is more desirable. Furthermore, as signal-to-noise ratio (SNR) of original images decreases due to reduced voxel size, the direct complex fitting procedure of myelin water imaging becomes more prone to systematic errors which severely compromised stability and reliability of the result. Instead of using the original part of T2* decay, this work presents a new method based on the WM-induced phase from tissue susceptibility calculated with the same mGRE dataset, in a three-pool WM model (water of myelin, axonal and extracellular water), to improve high resolution MWI. Compared with direct complex fitting for the higher spatial resolution case, the proposed method is shown to provide a more stable and accurate estimation of MWI parameters, and finer details near WM/GM boundaries with greatly reduced partial volume effects.

Field inhomogeneity correction for gradient echo myelin water fraction imaging.

Recently, the multi-echo gradient echo (MGRE) sequence has been proposed for multicomponent T2* (MC T2*) based myelin water fraction (MWF) mapping. This approach has appeal because it can provide fast whole-brain coverage, has low specific absorption rate, and short echo spacing. However, the MGRE signal requires correction for accurate MWF mapping, because of its sensitivity to magnetic field inhomogeneities (ΔB0 ). We propose a ΔB0 correction method for 2D MGRE data obtained for MWF mapping. Latter-echo MGRE data were fit to estimate B0 gradients in the slice-select direction ( Gz). The decay signal was corrected for the effects of Gz, and MC T2* analysis was performed using nonnegative least-squares fitting. The method was evaluated using simulations and its performance demonstrated in healthy volunteers. Simulations showed that MWFs are significantly biased in the presence of Gz and that our correction method leads to accurate MWF estimates. In vivo MWF maps obtained from corrected data showed recovery of MWF estimates in areas of high ΔB0, and overall good agreement with literature values obtained with the reference MC T2-based method. A new algorithm was presented for ΔB0 correction of 2D MGRE echo data acquired for MWF imaging. Simulations and in vivo data showed an improvement in MWF estimates. Magn Reson Med 78:49-57, 2017. © 2016 International Society for Magnetic Resonance in Medicine.