Duncan James S, Papademetris Xenophon, Yang Jing, Jackowski Marcel, Zeng Xiaolan, Staib Lawrence H
Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA.
Neuroimage. 2004;23 Suppl 1(Suppl 1):S34-45. doi: 10.1016/j.neuroimage.2004.07.027.
In this paper, we describe ongoing work in the Image Processing and Analysis Group (IPAG) at Yale University specifically aimed at the analysis of structural information as represented within magnetic resonance images (MRI) of the human brain. Specifically, we will describe our applied mathematical approaches to the segmentation of cortical and subcortical structure, the analysis of white matter fiber tracks using diffusion tensor imaging (DTI), and the intersubject registration of neuroanatomical (aMRI) data sets. Many of our methods rally around the use of geometric constraints, statistical (MAP) estimation, and the use of level set evolution strategies. The analysis of gray matter structure and connecting white matter paths combined with the ability to bring all information into a common space via intersubject registration should provide us with a rich set of data to investigate structure and variation in the human brain in neuropsychiatric disorders, as well as provide a basis for current work in the development of integrated brain function-structure analysis.
在本文中，我们描述了耶鲁大学图像处理与分析小组（IPAG）正在进行的工作，该工作专门针对人类大脑磁共振成像（MRI）中所呈现的结构信息进行分析。具体而言，我们将描述用于皮质和皮质下结构分割的应用数学方法、使用扩散张量成像（DTI）对白质纤维束进行分析，以及对神经解剖学（aMRI）数据集进行个体间配准。我们的许多方法围绕几何约束的使用、统计（MAP）估计以及水平集演化策略的使用展开。灰质结构和连接白质路径的分析，再加上通过个体间配准将所有信息带入公共空间的能力，应该为我们提供一组丰富的数据，用于研究神经精神疾病中人类大脑的结构和变异，同时也为当前综合脑功能 - 结构分析的发展工作提供基础。
