Kong Deping, Fan Yonghui, Hao Jinguang, Zhang Xiaofeng, Su Qingtang, Yao Tao, Zhang Caiming, Xiao Liang, Wang Gang
School of Information and Electrical Engineering, Ludong University, Yantai, China.
School of Computing, Informatics, And Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Comput Biol Med. 2020 May;120:103727. doi: 10.1016/j.compbiomed.2020.103727. Epub 2020 Mar 25.
Cortical thickness computation in magnetic resonance imaging (MRI) is an important method to study the brain morphological changes induced by neurodegenerative diseases. This paper presents an algorithm of thickness measurement based on a volumetric Laplacian operator (VLO), which is able to capture accurately the geometric information of brain images. The proposed algorithm is a novel three-step method: 1) The rule of parity and the shrinkage strategy are combined to detect and fix the intersection error regions between the cortical surface meshes separated by FreeSurfer software and the tetrahedral mesh is constructed which reflects the original morphological features of the cerebral cortex, 2) VLO and finite element method are combined to compute the temperature distribution in the cerebral cortex under the Dirichlet boundary conditions, and 3) the thermal gradient line is determined based on the constructed local isothermal surfaces and linear geometric interpolation results. Combined with half-face data storage structure, the cortical thickness can be computed accurately and effectively from the length of each gradient line. With the obtained thickness, we set experiments to study the group differences among groups of Alzheimer's disease (AD, N = 110), mild cognitive impairment (MCI, N = 101) and healthy control people (CTL, N = 128) by statistical analysis. The results show that the q-value associated with the group differences is 0.0458 between AD and CTL, 0.0371 between MCI and CTL, and 0.0044 between AD and MCI. Practical tests demonstrate that the algorithm of thickness measurement has high efficiency and is generic to be applied to various biological structures that have internal and external surfaces.
磁共振成像（MRI）中的皮质厚度计算是研究神经退行性疾病引起的脑形态变化的重要方法。本文提出了一种基于体积拉普拉斯算子（VLO）的厚度测量算法，该算法能够准确捕捉脑图像的几何信息。所提出的算法是一种新颖的三步法：1）结合奇偶规则和收缩策略来检测和修复由FreeSurfer软件分离的皮质表面网格之间的相交误差区域，并构建反映大脑皮质原始形态特征的四面体网格；2）将VLO与有限元方法相结合，在狄利克雷边界条件下计算大脑皮质中的温度分布；3）基于构建的局部等温面和线性几何插值结果确定热梯度线。结合半脸数据存储结构，可以根据每条梯度线的长度准确有效地计算皮质厚度。利用获得的厚度，我们通过统计分析设置实验来研究阿尔茨海默病（AD，N = 110）、轻度认知障碍（MCI，N = 101）和健康对照人群（CTL，N = 128）组间的差异。结果表明，AD与CTL之间的组间差异q值为0.0458，MCI与CTL之间为0.0371，AD与MCI之间为0.0044。实际测试表明，厚度测量算法具有高效率，并且普遍适用于具有内外表面的各种生物结构。
