使用字典学习器估计的各向同性体积分数的单壳NODDI
Single-Shell NODDI Using Dictionary Learner Estimated Isotropic Volume Fraction
摘要：
神经突方向弥散和密度成像（NODDI）能够从多壳层弥散MRI数据评估细胞内、细胞外和游离水信号。这是一个有见地的方法来表征脑组织的微观结构。NODDI参数的单壳重建在以前的研究中由于拟合失败而受到阻碍，特别是对于神经突密度指数（NDI）。在这里，我们研究了使用各向同性体积分数（fISO）作为先验，用单壳层数据创建鲁棒NODDI参数图的可能性。先验估计独立的NODDI模型约束，使用字典学习方法。首先，我们使用随机稀疏基于字典的网络（DictNet）来预测fISO，该网络是用从体内和模拟扩散MRI数据中获得的数据进行训练的。在单壳情况下，将平均扩散率（MD）和无扩散加权（S 0）的原始T2信号纳入词典中进行fISO估计。然后，NODDI框架与已知的fISO一起使用来估计NDI和取向分散指数（ODI）。在模拟中，我们的模型估计的fISO与其他fISO估计进行了比较。此外，使用合成数据模拟和在3 T扫描仪上收集的人类数据，我们比较了基于字典的学习先验NODDI（DLpN）与原始NODDI在单壳和多壳数据中的性能。我们的研究结果表明，DLpN衍生的NDI和ODI参数的单壳协议是与原始的多壳NODDI，和协议与b=2000 s/mm 2的表现最好（误差约5%的白质和灰质）。这可能允许NODDI通过对DictNet fISO训练的两个受试者进行多壳扫描来评估单壳数据的研究。
英文摘要：
Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO) as prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet) in predicting fISO which is trained with data obtained from in vivo and simulated diffusion MRI data. In single-shell cases, the mean diffusivity (MD) and raw T2 signal with no diffusion weighting (S0) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated by our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3T scanner, we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and protocol with b=2000 s/mm2 performs the best (error ~5% in white and grey matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.
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