Nunes Daniel, Cruz Tomás L, Jespersen Sune N, Shemesh Noam
Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Av. Brasilia 1400-038, Lisbon, Portugal.
Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
J Magn Reson. 2017 Apr;277:117-130. doi: 10.1016/j.jmr.2017.02.017. Epub 2017 Feb 28.
White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo.
白质（WM）微观结构，如轴突密度和平均直径，对中枢神经系统（CNS）的正常功能至关重要，因为它们与轴突传导速度密切相关。相反，这些微观结构特征的破坏可能导致严重的神经功能缺损，这表明对它们进行无创成像可能是迈向诊断和追踪病理生理学的重要一步。虽然已经提出基于扩散的MRI方法来描绘这些特征，但它们通常需要应用强大的梯度，而这在临床上很少具备，或者需要极长的采集方案来从参数密集型模型中提取信息。在本研究中，我们表明简单且省时的多梯度回波（MGE）MRI可用于从时间依赖性信号中由磁化率驱动的非单调衰减中提取轴突密度。我们在理论上和模拟中均表明，对于多隔室微观结构（如轴突和轴突外间隙，在此用作微观结构的简单模型）会发生非单调信号衰减，并且对于与主磁场平行的轴突，可以提取轴突密度。然后，我们在离体大鼠脊髓中通过实验证明，使用MGE衍生图谱可以清晰地区分具有不同微观结构特征的不同脊髓束。将定量结果与真实组织学结果进行比较时，它们反映了轴突分数（尽管存在偏差，如Bland-Altman分析所示）。此外，还可以估计轴突外分数。结果表明我们的模型过于简化，但同时也证明了使用简单且省时的MRI序列描绘潜在微观结构的方法具有潜力和实用性。我们进一步表明，一个简单的通用线性模型可以从四个模型参数预测平均轴突直径，并在脊髓中描绘这些平均轴突直径。虽然显然还需要进一步的建模和理论发展，但我们得出结论，重要的白质微观结构特征可以从简单、信噪比高效的多梯度回波MRI中提取，这为在体内更轻松地估计白质微观结构铺平了道路。
