Wang Gang, Zhang Xiaofeng, Su Qingtang, Shi Jie, Caselli Richard J, Wang Yalin
School of Information and Electrical Engineering, Ludong University, Yantai, China; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
School of Information and Electrical Engineering, Ludong University, Yantai, China.
Med Image Anal. 2015 May;22(1):1-20. doi: 10.1016/j.media.2015.01.005. Epub 2015 Feb 3.
Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.
磁共振成像（MRI）中的皮质厚度估计是研究大脑发育和神经退行性疾病的一项重要技术。本文提出了一种基于热核的皮质厚度估计算法，该算法由图谱和热核理论驱动，用于从活体脑磁共振（MR）图像中捕捉灰质几何信息。首先，我们构建一个与MR图像匹配并反映其固有几何特征的四面体网格。其次，通过体积拉普拉斯 - 贝尔特拉米算子计算调和场，并基于热核扩散追踪最大热传递概率来获得流线方向。由此我们可以计算软脑膜和白质表面上点之间的皮质厚度信息。新方法依赖于大脑的内在几何结构，计算稳健且准确。为了验证我们的算法，我们将其应用于阿尔茨海默病神经影像倡议（ADNI）数据集，研究与阿尔茨海默病（AD）和轻度认知障碍（MCI）相关的厚度差异。我们对151名受试者（51名AD患者、45名MCI患者、55名对照）的初步实验结果表明，新算法可能成功检测出AD、MCI患者与健康对照受试者之间具有统计学意义的差异。我们的计算框架高效且非常通用。它有潜力用于对任何具有明确界定的内表面和外表面的生物结构进行厚度估计。
