School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.
Neuroimage. 2013 Sep;78:111-34. doi: 10.1016/j.neuroimage.2013.04.018. Epub 2013 Apr 13.
In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.
本文提出了一种新的基于全纯 1-形式的曲面保角参数化、逆一致曲面流形配准和多元张量形态计量学（mTBM）的自动表面配准系统。首先，我们使用全纯 1-形式将曲面映射到一个规则的矩形空间。其次，我们通过结合曲面的局部保角因子和平均曲率来计算曲面的保角表示，并对保角表示的动态范围进行线性缩放，形成曲面的特征图像。然后，我们通过流形图像配准算法将特征图像与选定的模板图像对齐，该算法已经扩展到曲线坐标系中，以调整曲面参数化引入的失真。逆一致图像配准算法也被纳入到系统中，以联合估计研究和模板图像之间的正向和反向变换。这种对齐会在曲面上诱导出相应的变形。我们在阿尔茨海默病神经影像学倡议（ADNI）基线数据集上测试了该系统，以研究海马体上的 AD 症状。在我们的系统中，通过将海马体建模为 3D 参数曲面，我们将每个曲面与选定的模板曲面进行非线性配准。然后，我们使用 mTBM 来分析不同诊断组之间的形态计量学差异。实验结果表明，新系统的性能优于两个公开的皮质下表面配准工具：FIRST 和 SPHARM。我们还分析了载脂蛋白 E[元素的]4 等位基因（ApoE4）的遗传影响，ApoE4 被认为是 AD 最常见的风险因素。我们的工作成功地在轻度认知障碍（MCI）患者和健康对照组的 ApoE4 携带者和非携带者之间检测到了统计学上的显著差异。这些结果表明，ApoE 基因型可能与大脑萎缩加速有关，因此我们的工作提供了一种新的 MRI 分析工具，可能有助于 AD 的早期研究。
