RESOLVING WHITE MATTER TRACTS IN THE HUMAN BRAIN USING DIFFUSION WEIGHTED MAGNE

使用扩散加权磁解析人脑中的白质束

• 批准号: 7723336
• 负责人: MICHAEL F. COLE
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723336
• 关键词: Algorithms; Brain; Brain region; Computer Retrieval of Information on Scientific Projects Database; Computers; Diffusion; Diffusion Magnetic Resonance Imaging; Diffusion weighted imaging; Entropy; Funding; Grant; Human; Imaging technology; Institution; Life; Methods; Modeling; Research; Research Personnel; Resources; Slice; Source; Supercomputing; United States National Institutes of Health; Weight; base; day; white matter

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recently developed diffusion weighted imaging (DWI) technology provides an unprecedented view of anatomical connectivity in the living human brain. The most popular DWI modeling method is diffusion tensor imaging (DTI). Unfortunately, this method is severely limited in its ability to resolve white matter tract crossings, which occur quite often in the human brain (Alexander et al., 2002). We will use advanced DWI modeling methods, such as PAS-MRI, Q-Ball, and probabilistic tractography, to more accurately resolve white matter tract crossings and anatomical connectivity between brain regions. We plan to use supercomputing resources mostly for the computationally intensive PAS-MRI algorithm (based on maximum-entropy spherical deconvolution; Jansons et al., 2003 and Alexander et al., 2005). Resolving white matter tract crossings with this algorithm takes one minute per voxel on modern desktop computers. With 786432 voxels (128 X 128 in-plane, and 48 slices) it would take 546 days to compute just one subjects white matter tractography. We hope using supercomputing resources will make this a more practical endeavor.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 最近开发的扩散加权成像（DWI）技术提供了一个前所未有的视图解剖连接在活的人脑。最流行的DWI建模方法是扩散张量成像（DTI）。不幸的是，这种方法在分辨白色物质束交叉的能力方面受到严重限制，这种交叉经常发生在人脑中（亚历山大等人，2002年）。我们将使用先进的DWI建模方法，如PAS-MRI，Q-Ball和概率纤维束成像，以更准确地解决白色束交叉和大脑区域之间的解剖连接。我们计划将超级计算资源主要用于计算密集型PAS-MRI算法（基于最大熵球面去卷积; Jansons等人，2003和亚历山大等人，2005年）。在现代台式计算机上，使用该算法解决白色物质束交叉需要每体素一分钟。对于786432体素（128 X 128平面内，和48个切片），仅计算一个受试者的白色物质纤维束成像将花费546天。我们希望使用超级计算资源将使这成为一个更实际的奋进。