Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach
Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach
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DOI：
10.1016/j.neuroimage.2016.09.058
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发表时间：
2017-02-15
期刊：
影响因子：
5.7
通讯作者：
Kiselev, Valerij G.
中科院分区：
文献类型：
--
作者：
Reisert, Marco;Kellner, Elias;Kiselev, Valerij G.
扩散敏化磁共振成像探测人类大脑的细胞结构，但主要的微观结构信息在平均更高层次，介观组织组织（如神经元纤维的不同方向）时丢失。虽然由于有限的成像分辨率，这种平均是不可避免的，但我们提出了一种从介观结构的影响中分离微观细胞特性的方法。我们进一步避免了经典的拟合范式，并根据贝叶斯估计器使用监督机器学习来估计微观结构特性。该方法可以找到给定微观结构模型的可检测参数，并在几秒钟内计算出来，这使得它适用于广泛的神经科学应用。
Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.