MSPA-MCS: Mathematical and Computational Algorithms for Visualization of Human Brain Neural Pathways

MSPA-MCS：人脑神经通路可视化的数学和计算算法

• 批准号: 0527967
• 负责人: Jun Zhang
• 金额: --
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0527967&HistoricalAwards=false
• 关键词: MSPA; MCS; Mathematical; Computational; Algorithms

This project will conduct a preliminary but solid study on developing a new mathematical model in the application of a novel magnetic resonance imaging technique to reconstruct human brain neural pathways. The new model will make use of the high angular resolution diffusion-weighted (HARD) imaging technique and the diffusion equation simulation based fiber tractography technique to trace white matter fiber tracts in the human brain.The robust diffusion equation simulation based fiber tracking technique has been used with standard second-order diffusion tensor magnetic resonance imaging (DT-MRI) data and some major white matter fiber tracts have been reconstructed. Compared with the standard DT-MRI based techniques, the fusion of HARDtechnique and diffusion equation simulation based tractography is a combination of more accurate data acquisition technique with more sensitive analysis technique and is likely to produce a much more advanced visualization technique that allows scientists to "see through" the human brain noninvasively and understand its in vivo functions and connectivities. This understanding can help scientists to design biologically motivated computing systems and combat many brain diseases. In addition, this project emphasizes training Ph.D. students as the next generation interdisciplinary scientists with solid background in mathematics, computer science, and neoruscience.

该项目将进行初步但扎实的研究，开发一种新的数学模型，应用新型磁共振成像技术来重建人脑神经通路。新模型将利用高角分辨率扩散加权（HARD）成像技术和基于扩散方程模拟的纤维束成像技术来追踪人脑中的白质纤维束。与标准二阶扩散张量磁共振成像（DT-MRI）数据一起使用，一些主要的白质纤维束已被重建。与基于标准 DT-MRI 的技术相比，HARD 技术和基于扩散方程模拟的纤维束成像技术的融合是更准确的数据采集技术与更灵敏的分析技术的结合，并且可能产生更先进的可视化技术，使科学家能够“无创地透视“人脑并了解其体内功能和连接性。这种理解可以帮助科学家设计生物驱动的计算系统并对抗许多脑部疾病。此外，该项目强调培养博士。学生成为具有数学、计算机科学和新科学扎实背景的下一代跨学科科学家。