Falangola M F, Ardekani B A, Lee S-P, Babb J S, Bogart A, Dyakin V V, Nixon R, Duff K, Helpern J A
Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA.
J Neurosci Methods. 2005 May 15;144(1):91-7. doi: 10.1016/j.jneumeth.2004.10.012. Epub 2004 Dec 7.
Transgenic mouse models have been essential for understanding the pathogenesis of Alzheimer's disease (AD) including those that model the deposition process of beta-amyloid (Abeta). Several laboratories have focused on research related to the non-invasive detection of early changes in brains of transgenic mouse models of Alzheimer's pathology. Most of this work has been performed using regional image analysis of individual mouse brains and pooling the results for statistical assessment. Here we report the implementation of a non-linear image registration algorithm to register anatomical and transverse relaxation time (T2) maps estimated from MR images of transgenic mice. The algorithm successfully registered mouse brain magnetic resonance imaging (MRI) volumes and T2 maps, allowing reliable estimates of T2 values for different regions of interest from the resultant combined images. This approach significantly reduced the data processing and analysis time, and improved the ability to statistically discriminate between groups. Additionally, 3D visualization of intra-regional distributions of T2 of the resultant registered images provided the ability to detect small changes between groups that otherwise would not be possible to detect.
转基因小鼠模型对于理解阿尔茨海默病（AD）的发病机制至关重要，包括那些模拟β-淀粉样蛋白（Aβ）沉积过程的模型。几个实验室专注于与阿尔茨海默病病理转基因小鼠模型大脑早期变化的无创检测相关的研究。这项工作大多是通过对单个小鼠大脑进行区域图像分析并汇总结果进行统计评估来完成的。在此，我们报告了一种非线性图像配准算法的实施，用于配准从转基因小鼠的磁共振图像估计的解剖图像和横向弛豫时间（T2）图。该算法成功地配准了小鼠脑磁共振成像（MRI）体积和T2图，从而能够从所得的组合图像中可靠地估计不同感兴趣区域的T2值。这种方法显著减少了数据处理和分析时间，并提高了在组间进行统计区分的能力。此外，所得配准图像的T2区域内分布的三维可视化提供了检测组间微小变化的能力，否则这些变化是无法检测到的。
