Chou Yi-Yu, Leporé Natasha, Madsen Sarah K, Saharan Priya, Hua Xue, Jack Clifford R, Shaw Leslie M, Trojanowski John Q, Weiner Michael W, Toga Arthur W, Thompson Paul M
Laboratory of Neuro Imaging, Dept. Neurology, UCLA School of Medicine, Los Angeles, CA.
Mayo Clinic, Rochester, MN.
Proc IEEE Int Symp Biomed Imaging. 2010 Apr;2010:241-244. doi: 10.1109/ISBI.2010.5490368. Epub 2010 Jun 21.
There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to predict future decline, and should be computable automatically from hundreds to thousands of images without user intervention. Here we used our multi-atlas fluid image alignment method (MAFIA [1]), to automatically segment parametric 3D surface models of the lateral ventricles in brain MRI scans from 184 AD, 391 MCI, and 229 healthy elderly controls. Radial expansion of the ventricles, computed pointwise, was correlated with measures of (1) clinical decline, (2) pathology from CSF, and (3) future deterioration. Surface-based correlation maps were assessed using a cumulative distribution function method to rank influential covariates according to their effect sizes. The resulting approach is highly automated, and boosts the power of fluid image registration by integrating multiple independent registrations to reduce segmentation errors.
迫切需要与认知衰退以及脑脊液（CSF）中可检测到的公认病理学指标相关的阿尔茨海默病（AD）神经影像生物标志物。理想的生物标志物还应能够预测未来的衰退，并且应该能够在无需用户干预的情况下，从数百张到数千张图像中自动计算得出。在这里，我们使用了我们的多图谱流体图像配准方法（MAFIA [1]），对来自184名AD患者、391名轻度认知障碍（MCI）患者和229名健康老年对照的脑部MRI扫描图像中的侧脑室参数化3D表面模型进行自动分割。逐点计算的脑室径向扩张与以下指标相关：（1）临床衰退，（2）脑脊液病理学指标，以及（3）未来病情恶化。基于表面的相关图使用累积分布函数方法进行评估，以便根据效应大小对有影响的协变量进行排名。由此产生的方法高度自动化，并通过整合多个独立配准以减少分割误差，提高了流体图像配准的效能。
