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Purpose: To demonstrate robust myelin water fraction (MWF) mapping using an artificial neural network (ANN) with multi-echo gradient-echo (GRE) signal. Methods: 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. Results: 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. Conclusion: 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. K E Y W O R D S artificial neural network, multi-echo gradient echo, myelin water imaging, T * 2 distribution [Correction added after online publication 18 August, 2020. The authors have corrected the spelling of author name Won-Jin Moon.] | 381 JUNG et al. 1 | INTRODUCTION Myelin water fraction (MWF) as a method for measuring quantitative myelin signals has demonstrated potential to diagnose various demyelinating diseases such as multiple sclerosis, schizophrenia, and stroke. 1,2 Conventional myelin water imaging (MWI) uses multi-echo spin-echo acquisition and nonnegative least-squares estimation, 3,4 whereas more recently multi-echo gradient echo (mGRE) has been suggested. 5-9 These methods provide benefits such as faster acquisition time and lower specific absorption rates. Several studies have proposed methods to acquire high-quality MWI data using mGRE. Such studies suggest applying the nonlinear least-squares (NLLS) algorithm to the acquired signal using a defined model, such as the three-pool exponential model. 8,10 These methods are based on the assumption that the white-matter (WM) water can be reliably modeled by three-pool exponential components with individual frequency shifts. 5,6,8 These methods can be further improved by physiological noise compensation 11 and B 0 field inhomogeneity correction. 10,12,13 Despite these developments, there are still challenges in improving the accuracy and robustness of the MWF. The NLLS (used for estimating MWFs) has been reported to be inaccurate and unstable, especially at low-to-moderate SNRs. 14-16 This requires high SNR data acquisition for MWFs, limiting the scan...

Purpose To investigate the effect of the range of TE on multi‐echo gradient echo–based myelin water fraction (MWF) mapping at 3 T. Methods 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. Results 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. Conclusion 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.

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 T * 2 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-decayingcomponent 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.

This study demonstrates that high-SNR multiple T (*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2 J. MAGN. RESON. IMAGING 2017;45:1835-1845.

The proposed method improves zero-TE phase extrapolation, especially in regions of nonlinear phase evolution. Improved conductivity imaging using mGRE can be performed.

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