Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares
Revisiting the T2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares
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DOI：
10.1016/j.neuroimage.2021.118582
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发表时间：
2021-09-22
期刊：
影响因子：
5.7
通讯作者：
Thiran, Jean-Philippe
中科院分区：
文献类型：
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作者：
Canales-Rodriguez, Erick Jorge;Pizzolato, Marco;Thiran, Jean-Philippe
多回波T-2磁共振图像包含分区水的T-2弛豫时间分布的信息，从中我们可以估计相关的脑组织属性，如髓鞘水分数(MWF)。正则化非负最小二乘(NNLS)是估计非参数T-2谱的首选工具。然而，该估计是病态的，对噪声敏感，并且受所采用的正则化权重的影响很大。这项研究的目的有三个：首先，我们想要强调，对于求解反问题，使用两种明显无害的参数化法，我们称之为标准正则化形式和替代正则化形式，会导致不同的解；其次，评估这两种参数化法的性能；以及第三，提出一种新的贝叶斯正则化NNLS方法(BayesReg)。使用两种正则化形式，将BayesReg与两种传统的方法(L曲线和卡方(X-2)拟合)的性能进行了比较。我们生成了一个合成数据的大型数据集，获取了健康参与者的活体人脑数据，用于进行扫描-重新扫描分析，并将来自组织学的髓鞘含量与从体外数据估计的MWF进行了关联。合成数据的结果表明，BayesReg提供了准确的MWF估计，与L曲线和X-2的估计相当，并且在更宽的信噪比范围内具有更好的整体稳定性。值得注意的是，我们通过使用替代正则化形式获得了更好的结果。这项研究中报告的相关性高于使用相同的体外和组织学数据的先前研究中报告的相关性。在人脑数据中，由L曲线和贝叶斯-REG估计的地图更具重复性。然而，与L曲线相比，BayesReg曲线产生的T-2谱受超平滑的影响较小。这些发现表明BayesReg是估计T-2分布和MWF图的一个很好的替代方法。
Multi-echo T-2 magnetic resonance images contain information about the distribution of T-2 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 T-2 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 (X-2) 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 X-2, 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 T-2 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 T-2 distributions and MWF maps.