A new analysis approach for T₂ relaxometry myelin water quantification: Orthogonal Matching Pursuit

Abstract

Purpose In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T 2 relaxation is a powerful and widely applied method for the quantification of the myelin water fraction (MWF). In recent literature, the MWF is most commonly estimated using a regularized nonnegative least squares algorithm. Methods The orthogonal matching pursuit algorithm is proposed as an alternative method for the estimation of the MWF. The orthogonal matching pursuit is a greedy sparse reconstruction algorithm with a low computation complexity. For validation, both methods are compared to a ground truth using numerical simulations and a phantom model using comparable computation times. The numerical simulations were used to measure the theoretical errors, as well as the effects of varying the SNR, strength of the regularization, and resolution of the basis set. Additionally, a phantom model was used to estimate the performance of the 2 methods while including errors occurring due to the MR measurement. Lastly, 4 healthy subjects were scanned to evaluate the in vivo performance. Results The results in simulations and phantoms demonstrate that the MWFs determined with the orthogonal matching pursuit are 1.7 times more accurate as compared to the nonnegative least squares, with a comparable precision. The remaining bias of the MWF is shown to be related to the regularization of the nonnegative least squares algorithm and the Rician noise present in magnitude MR images. Conclusion The orthogonal matching pursuit algorithm provides a more accurate alternative for T 2 relaxometry myelin water quantification.

Artificial neural network for myelin water imaging

“…Changing this factor has an impact on the results of the T2 decomposition 39 40 to improve the performance.…”

“…Changing this factor has an impact on the results of the T2 decomposition 39 . Therefore, the networks need to be retrained if new regularization parameters are desirable.…”

Improved estimation of myelin water fractions with learned parameter distributions

“…Several methods have been proposed recently to address the robustness issue with MWF estimation. Some of these methods denoise the measured data before parameter estimation by imposing low-rank constraints 17,18 or sparsity constraints, 18 whereas others apply constraints directly on the model parameters using constraints such as spectral and local spatial smoothness, [19][20][21][22] nonlocal similarity, 23 local sparsity, 24 and joint sparsity. 25,26 Our proposed method is complementary to these methods; they can be integrated to further improve MWF estimation.…”

“…15,16 To address this issue, several methods have been developed to denoise the measured data before parameter estimation by imposing low-rank constraints 17,18 or sparsity constraints. 18 There are also several methods that apply constraints directly on the model parameters using constraints such as spectral and local spatial smoothness, [19][20][21][22] nonlocal similarity, 23 local sparsity, 24 and joint sparsity. 25,26 More recently, deep learning-based methods have also been proposed to use prior information embedded in the training data to improve MWF estimation, producing promising results.…”

Parsimonious discretization for characterizing multi‐exponential decay in magnetic resonance

“…Free induction decays, [1][2][3][4] refocused echo trains [5][6][7] and diffusion-weighted signals [8][9][10][11] are all examples of MR signals that represent a superposition of monoexponentially-decaying component signals. Multi-exponential analysis to define component amplitudes and decay time constants is then applied with the goal of identifying important structural or functional tissue characteristics.…”