IMPROVEMENTS IN DIFFUSION TENSOR IMAGING (DTI)

扩散张量成像 (DTI) 的改进

• 批准号: 7955350
• 负责人: Michael E Moseley
• 金额: $ 2.41万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955350
• 关键词: Algorithms; Brain; Brain region; Cognitive; Computer Retrieval of Information on Scientific Projects Database; Computer software; Diffusion Magnetic Resonance Imaging; Fiber; Funding; Grant; Institution; Maps; Methods; Modeling; Motor; Pathway interactions; Pattern; Plants; Process; Protocols documentation; Reproducibility; Research; Research Personnel; Resources; Seeds; Source; Techniques; Technology; Time; United States National Institutes of Health; Writing; image reconstruction; trafficking; vector; 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. The overall aim of this project is to map the potential pathways of cognitive and motor activation. This has progressed in the last year to add several improvements in the DTI image reconstruction and processing software (?Tensorcalc?) that we wrote and maintain for the P41 RR09784. Diffusion tensor imaging (DTI) is a promising new technique for the assessment of white matter (WM) structural integrity and connectivity. We have written and refined 3 key core DTI map tracking algorithms that are being used to track white matter fibers for determining the potential white matter trafficking patterns. One of the more exciting methods to arise from the noninvasive mapping of white matter tracks is the potential of tracking the white matter fibers from one region of brain to another. This in essence, reveals the underlying ?wiring? of the activated brain. Our algorithm allows for real-time connectivity maps that can be generated from planting a seed in one white matter tract region. We have constructed software ?phantoms? to check the algorithms for accuracy and reproducibility. Our diffusion tensor imaging protocol is performed using a spin echo EPI technique and we have added a host of new spiral methods. The vector maps hold the orientation of the fibers as well as the magnitude of the orientation. From the fiber maps, one can compose several general approaches to finding connectivity from fiber to fiber from a variety of model algorithms that we have built.

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。 该项目的总体目标是绘制认知和运动激活的潜在途径。在过去的一年里，这一工作取得了进展，在DTI图像重建和处理软件中增加了几项改进（？Tensorcalc？）我们为P41 RR 09784编写并维护的。扩散张量成像（DTI）是一种很有前途的新技术，用于评估白色物质（WM）的结构完整性和连接性。我们已经编写并完善了3个关键核心DTI图跟踪算法，用于跟踪白色物质纤维，以确定潜在的白色物质运输模式。从白色物质轨迹的非侵入性映射中产生的更令人兴奋的方法之一是从大脑的一个区域到另一个区域跟踪白色物质纤维的潜力。这在本质上，揭示了潜在的？接线？激活的大脑。我们的算法允许实时连接地图，可以从种植种子在一个白色物质道区域。我们已经构建了软件？幻影？检查算法的准确性和可重复性我们的扩散张量成像协议是使用自旋回波EPI技术进行的，我们增加了一系列新的螺旋方法。矢量图保存纤维的方向以及方向的大小。从纤维地图，可以组成几个通用的方法来找到连接从纤维到纤维从各种模型算法，我们已经建立。