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定量磁化率图的纹理分析用于区分阿尔茨海默病与认知正常及轻度认知障碍。

Texture analyses of quantitative susceptibility maps to differentiate Alzheimer's disease from cognitive normal and mild cognitive impairment.

作者信息

Hwang Eo-Jin, Kim Hyug-Gi, Kim Danbi, Rhee Hak Young, Ryu Chang-Woo, Liu Tian, Wang Yi, Jahng Geon-Ho

机构信息

Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, #892 Dongnam-ro, Gangdong-Gu, Seoul 05278, South Korea.

Department of Biomedical Engineering, Graduate School, Kyung Hee University, #1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, South Korea.

出版信息

Med Phys. 2016 Aug;43(8):4718. doi: 10.1118/1.4958959.

Abstract

PURPOSE

Although a number of studies have focused on finding anatomical regions in which iron concentrations are high, no study has been conducted to examine the overall variations in susceptibility maps of Alzheimer's disease (AD). The objective of this study, therefore, was to differentiate AD from cognitive normal (CN) and mild cognitive impairment (MCI) using a texture analysis of quantitative susceptibility maps (QSMs).

METHODS

The study was approved by the local institutional review board, and informed consent was obtained from all subjects. In each participant group-CN, MCI, and AD-18 elderly subjects were enrolled. A fully first-order flow-compensated 3D gradient-echo sequence was run to obtain axial magnitudes and phase images and to produce QSM data. Sagittal structural 3D T1-weighted (3DT1W) images were also obtained with the magnetization-prepared rapid acquisition of gradient-echo sequence to obtain brain tissue images. The first- and second-order texture parameters of the QSMs and 3DT1W images were obtained to evaluate group differences using a one-way analysis of covariance.

RESULTS

For the first-order QSM analysis, mean, standard deviation, and covariance of signal intensity separated the subject groups (F = 5.191, p = 0.009). For the second-order analysis, angular second moment, contrast, and correlation separated the subject groups (F = 6.896, p = 0.002). Finally, a receiver operating characteristic curve analysis differentiated MCI from CN in white matter on the QSMs (z = 3.092, p = 0.0020).

CONCLUSIONS

This was the first study to evaluate the textures of QSM in AD, which overcame the limitations of voxel-based analyses. The QSM texture analysis successfully distinguished both AD and MCI from CN and outperformed the voxel-based analysis using 3DT1-weighed images in separating MCI from CN. The first-order textures were more efficient in differentiating MCI from CN than did the second-order.

摘要

目的

尽管已有多项研究致力于寻找铁浓度较高的解剖区域,但尚未有研究对阿尔茨海默病(AD)的磁化率图谱的整体变化进行考察。因此,本研究的目的是通过定量磁化率图谱(QSM)的纹理分析,将AD与认知正常(CN)及轻度认知障碍(MCI)区分开来。

方法

本研究经当地机构审查委员会批准,并获得所有受试者的知情同意。在每个参与者组(CN、MCI和AD)中,招募了18名老年受试者。运行一个完全一阶流动补偿的3D梯度回波序列,以获取轴向幅度和相位图像,并生成QSM数据。还使用磁化准备快速采集梯度回波序列获取矢状结构3D T1加权(3DT1W)图像,以获得脑组织图像。获取QSM和3DT1W图像的一阶和二阶纹理参数,使用单因素协方差分析评估组间差异。

结果

对于一阶QSM分析,信号强度的均值、标准差和协方差可区分各受试者组(F = 5.191,p = 0.009)。对于二阶分析,角二阶矩、对比度和相关性可区分各受试者组(F = 6.896,p = 0.002)。最后,受试者工作特征曲线分析在QSM的白质中区分了MCI和CN(z = 3.092,p = 0.0020)。

结论

这是第一项评估AD中QSM纹理的研究,克服了基于体素分析的局限性。QSM纹理分析成功地将AD和MCI与CN区分开来,并且在将MCI与CN区分开方面优于使用基于体素分析的3DT1加权图像。一阶纹理在区分MCI和CN方面比二阶纹理更有效。

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