MAPL: Tissue microstructure estimation using Laplacian- regularized MAP-MRI and its application to HCP data
MAPL: Tissue microstructure estimation using Laplacian- regularized MAP-MRI and its application to HCP data
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DOI：
10.1016/j.neuroimage.2016.03.046
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发表时间：
2016-07-01
期刊：
影响因子：
5.7
通讯作者：
Deriche, Rachid
中科院分区：
文献类型：
--
作者：
Fick, Rutger H. J.;Wassermann, Demian;Deriche, Rachid
脑白色物质的微结构相关特征的恢复是扩散MRI当前的挑战。为了从多壳层扩散MRI数据中鲁棒地估计这些重要特征，我们建议使用重构信号的拉普拉斯算子的范数来解析地正则化平均表观扩散器（MAP）-MRI方法的系数估计。我们首先比较我们的方法，我们称之为MAPL，与竞争，国家的最先进的功能基础的方法。我们表明，它优于原来的MAP-MRI的实现和最近提出的修改球面极傅立叶（mSPF）的基础上，信号拟合和重建的包围平均放大器（EAP）和方向分布函数（ODF）的噪声，稀疏采样的数据与参考金标准数据的物理体模。然后，为了减少使用多室组织模型的参数估计的方差，我们建议使用MAPL的信号拟合和外推作为预处理步骤。我们研究了MAPL对使用简化的Axcaliber模型估计轴突直径和使用轴突取向分散和密度成像（NODDI）模型估计轴突分散的影响。我们使用它作为预处理步骤，估计和减少这些参数的方差在胼胝体的MGH人类连接组项目的六个不同的主题的积极影响。最后，我们将估计的轴突直径、分散度和受限体积分数与各向异性分数（FA）相关联，并清楚地表明FA的变化与所有估计参数的变化显著相关。总体而言，我们说明了使用良好正则化的功能基础以及多室方法来恢复具有更小变异性的重要微观结构组织参数的潜力，从而有助于更好地理解大脑白色物质的微观结构相关特征的挑战。(C)2016 Elsevier Inc. All rights reserved.
The recovery of microstructure-related features of the brain's white matter is a current challenge in diffusion MRI. To robustly estimate these important features from multi-shell diffusion MRI data, we propose to analytically regularize the coefficient estimation of the Mean Apparent Propagator (MAP)-MRI method using the norm of the Laplacian of the reconstructed signal. We first compare our approach, which we call MAPL, with competing, state-of-the-art functional basis approaches. We show that it outperforms the original MAP-MRI implementation and the recently proposed modified Spherical Polar Fourier (mSPF) basis with respect to signal fitting and reconstruction of the Ensemble Average Propagator (EAP) and Orientation Distribution Function (ODF) in noisy, sparsely sampled data of a physical phantom with reference gold standard data. Then, to reduce the variance of parameter estimation using multi-compartment tissue models, we propose to use MAPL's signal fitting and extrapolation as a preprocessing step. We study the effect of MAPL on the estimation of axon diameter using a simplified Axcaliber model and axonal dispersion using the Neurite Orientation Dispersion and Density Imaging (NODDI) model. We show the positive effect of using it as a preprocessing step in estimating and reducing the variances of these parameters in the Corpus Callosum of six different subjects of the MGH Human Connectome Project. Finally, we correlate the estimated axon diameter, dispersion and restricted volume fractions with Fractional Anisotropy ( FA) and clearly show that changes in FA significantly correlate with changes in all estimated parameters.Overall, we illustrate the potential of using a well-regularized functional basis together with multi-compartment approaches to recover important microstructure tissue parameters with much less variability, thus contributing to the challenge of better understanding microstructure-related features of the brain's white matter. (C) 2016 Elsevier Inc. All rights reserved.