多模态神经影像数据的综合信息可视化

Guangyu Zou, Jing Hua, Ming Dong
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引用次数: 9

摘要

本文提出了一种用于跨学科神经影像学数据分析的新型综合信息可视化框架。该框架可以整合不同成像方式捕获的多模态信息和不同主体提供的基于人群的统计信息。在此框架下,皮质结构的准确配准是跨种群信息整合的基础。我们提出了一种采用共形结构和球面薄板样条的非刚性主体间脑表面配准方法。球面薄板样条被设计为明确匹配突出的同源地标,同时在球面上插值一个全局变形场,在变换后的空间中注册脑表面。在此基础上,提出了一种综合信息融合与可视化的方法来处理多模态神经影像数据。整个框架证明了它在跨主题的多模态神经成像数据分析中的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrative Information Visualization of Multimodality Neuroimaging Data
This paper presents a novel integrative information visualization framework for cross-subject neuroimaging data analysis. The framework can integrate multimodal information captured by different imaging modalities and population-based statistical information presented by different subjects. In this framework, accurate registration of cortical structures is the foundation for the information integration across population. We present a non-rigid intersubject brain surface registration method using conformal structure and spherical thin-plate splines. Spherical thin-plate splines are designed to explicitly match prominent homologous landmarks, and meanwhile, interpolate a global deformation field on the spherical domain, registering brain surfaces in a transformed space. Subsequently, an approach for the integrative information fusion and visualization is presented to handle multimodality neuroimaging data. The entire framework demonstrates its usefulness in multimodality neuroimaging data analysis across subjects.
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