On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol.
On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol.
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DOI：
10.1016/j.neuroimage.2021.118303
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发表时间：
2021-10-01
期刊：
影响因子：
5.7
通讯作者：
Kaden E
中科院分区：
文献类型：
--
作者：
Gyori NG;Clark CA;Alexander DC;Kaden E
B-张量编码使得能够估计大脑中的球形细胞结构。球形隔室可以提供明显的神经索马密度的标记。可以使用深度学习以快速和鲁棒的方式估计模型参数。在广泛使用的临床扫描仪上可以实现实际的采集时间。扩散MRI是一种非侵入性探测脑组织微结构的有价值的工具。今天，基于模型的技术被广泛使用，并用于白色物质表征，其中它们的发展相对成熟。相反，灰质中的组织建模更具挑战性，并且不存在普遍接受的模型。随着测量技术和建模工作的进步，一种临床可行的技术，揭示灰质微结构的显着特征，如准球形细胞体和准圆柱形细胞投影的密度，是一个令人兴奋的前景。作为在临床可行的设置中捕获灰质的微观结构的一步，这项工作使用了一个生物物理模型，该模型被设计为在存在方向异质性的情况下解开球形和圆柱形结构的扩散特征，并利用B张量编码测量，与标准的单扩散编码序列相比，它提供了额外的灵敏度。为了快速和稳健地估计微观结构参数，我们利用机器学习的最新进展，用人工神经网络取代传统的拟合技术，在几秒钟内拟合复杂的生物物理模型。我们的研究结果表明，在健康的人类受试者的球形和圆柱形的几何形状的明显标志，特别是增加的体积分数相比，白色物质的灰质的球形隔室。我们评估在何种程度上球形和圆柱形的几何形状可以解释为相关的神经索马和神经预测，分别和量化参数估计误差的存在下，从建模假设的各种偏离。虽然进一步的工作是必要的，以翻译的想法在这项工作中的临床，我们建议，生物标志物集中在准球形细胞几何形状可能是有价值的神经发育障碍和神经退行性疾病的增强评估。
B-tensor encoding enables estimation of spherical cellular structures in the brain. Spherical compartments may provide markers for apparent neural soma density. Model parameters can be estimated in a fast and robust way using deep learning. Practical acquisition times are achievable on widely available clinical scanners. Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.
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影响因子：
5.7
作者：
Clark, CA;Barrick, TR;Bell, BA
通讯作者：
Bell, BA
DOI：
10.1006/jmrb.1994.1037
发表时间：
1994-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
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通讯作者：
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影响因子：
3.3
作者：
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通讯作者：
Ugurbil, Kamil
影响因子：
3.3
作者：
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通讯作者：
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影响因子：
4.8
作者：
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通讯作者：
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