Wang Gang, Wang Yalin
School of Information and Electrical Engineering, Ludong University, Yantai, Shandong 264025, China.
Arizona State University, School of Computing, Informatics, Decision Systems Engineering, 699 S. Mill Avenue, Tempe, AZ 85281, United States.
Neuroimage. 2017 Feb 15;147:360-380. doi: 10.1016/j.neuroimage.2016.12.014. Epub 2016 Dec 26.
In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.
在本文中，我们提出了一种基于热核的区域形状描述符，它可能比其他现有方法更能充分利用体积形态信息，从而提高脑磁共振成像（MRI）分析的统计功效。我们的分析机制由图谱和热核理论驱动，以捕捉构建的四面体网格中的体积几何信息。为了捕捉大脑灰质形状的深刻变化，我们首先使用体积拉普拉斯 - 贝尔特拉米算子，通过计算四面体网格中的流线来确定白质 - 灰质和脑脊液 - 灰质边界表面之间的点对对应关系。其次，我们提出多尺度灰质形态特征来描述点对之间随机游走的转移概率，这反映了内在的几何特征。第三，应用点分布模型来降低灰质形态特征的维度并生成内部结构特征。通过稀疏线性判别分析，我们选择了一个具有更高分类准确率的简洁形态特征集。在我们的实验中，在阿尔茨海默病神经影像倡议组织的公开可用数据上，对于阿尔茨海默病及其前驱阶段，即轻度认知障碍的分类，我们提出的方法优于FreeSurfer软件计算的皮质厚度特征。基于多尺度和物理的体积结构特征可能比一些传统的基于MRI的灰质形态分析方法具有更强的统计功效。
