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通过特定疾病模式的脑硬度和阻尼比来识别正常压力脑积水。

Identification of Normal Pressure Hydrocephalus by Disease-Specific Patterns of Brain Stiffness and Damping Ratio.

机构信息

From the Departments of Radiology.

Physiology and Biomedical Engineering.

出版信息

Invest Radiol. 2020 Apr;55(4):200-208. doi: 10.1097/RLI.0000000000000630.

Abstract

OBJECTIVES

The aim of this study was to perform a whole-brain analysis of alterations in brain mechanical properties due to normal pressure hydrocephalus (NPH).

MATERIALS AND METHODS

Magnetic resonance elastography (MRE) examinations were performed on 85 participants, including 44 cognitively unimpaired controls, 33 with NPH, and 8 who were amyloid-positive with Alzheimer clinical syndrome. A custom neural network inversion was used to estimate stiffness and damping ratio from patches of displacement data, accounting for edges by training the network to estimate the mechanical properties in the presence of missing data. This learned inversion was first compared with a standard analytical approach in simulation experiments and then applied to the in vivo MRE measurements. The effect of NPH on the mechanical properties was then assessed by voxel-wise modeling of the stiffness and damping ratio maps. Finally, a pattern analysis was performed on each individual's mechanical property maps by computing the correlation between each person's maps with the expected NPH effect. These features were used to fit a classifier and assess diagnostic accuracy.

RESULTS

The voxel-wise analysis of the in vivo mechanical property maps revealed a unique pattern in participants with NPH, including a concentric pattern of stiffening near the dural surface and softening near the ventricles, as well as decreased damping ratio predominantly in superior regions of the white matter (family-wise error corrected P < 0.05 at cluster level). The pattern of viscoelastic changes in each participant predicted NPH status in this cohort, separating participants with NPH from the control and the amyloid-positive with Alzheimer clinical syndrome groups, with areas under the receiver operating characteristic curve of 0.999 and 1, respectively.

CONCLUSIONS

This study provides motivation for further development of the neural network inversion framework and demonstrates the potential of MRE as a novel tool to diagnose NPH and provide a window into its pathogenesis.

摘要

目的

本研究旨在对正常压力脑积水(NPH)引起的脑力学性质改变进行全脑分析。

材料与方法

对 85 名参与者进行磁共振弹性成像(MRE)检查,包括 44 名认知正常的对照组、33 名 NPH 组和 8 名有阿尔茨海默病临床综合征的淀粉样蛋白阳性组。使用自定义神经网络反演方法,从位移数据斑块中估计刚度和阻尼比,通过训练网络在存在缺失数据的情况下估计力学性质来解决边缘问题。该学习反演方法首先在模拟实验中与标准分析方法进行比较,然后应用于体内 MRE 测量。然后通过对刚度和阻尼比图的体素建模来评估 NPH 对力学性质的影响。最后,通过计算每个人的力学性质图与预期 NPH 效应之间的相关性,对每个人的力学性质图进行模式分析。这些特征用于拟合分类器并评估诊断准确性。

结果

对体内力学性质图的体素分析显示,NPH 参与者具有独特的模式,包括硬脑膜表面附近的僵硬和脑室附近的软化的同心模式,以及白质上部区域的阻尼比降低(簇水平的校正后的错误发现率 P < 0.05)。每个参与者的粘弹性变化模式预测了该队列中 NPH 状态,将 NPH 参与者与对照组和阿尔茨海默病临床综合征淀粉样蛋白阳性组分开,受试者工作特征曲线下面积分别为 0.999 和 1。

结论

本研究为进一步开发神经网络反演框架提供了动力,并证明了 MRE 作为诊断 NPH 并提供其发病机制窗口的新型工具的潜力。

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