Software for the Analysis and Visualization of High Angular Resolution DTI Data

用于高角分辨率 DTI 数据分析和可视化的软件

• 批准号: 7477670
• 负责人: LAWRENCE R FRANK
• 金额: $ 23.63万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7477670
• 关键词: Address; Affect; Alcohol abuse; Anatomy; Architecture; Bioinformatics; Brain; Characteristics; Class; Complex; Computational Biology; Computer software; Data; Data Analyses; Data Set; Development; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Documentation; Environment; Equation; Fetal Alcohol Syndrome; Fiber; Goals; Image; Image Analysis; Imagery; Investigation; Language Disorders; Location; Magnetic Resonance Imaging; Maintenance; Maps; Measurement; Measures; Methods; Modeling; Molecular; Motion; Motivation; Nature; Neurosciences Research; Patients; Personal Satisfaction; Physiologic pulse; Physiology; Placement; Principal Investigator; Pulse taking; Research; Resolution; Seeds; Signal Transduction; Solutions; Standards of Weights and Measures; Structure; Tissues; Uncertainty; Variant; Vendor; Water; White Matter Disease; Work; Writing; analytical method; base; data space; diffusion anisotropy; improved; myelination; neuroimaging; open source; programs; research study; response; software development; tool; white matter

DESCRIPTION (provided by applicant): The diffusion anisotropy (DA) in the brain measured by diffusion tensor imaging (DTI) is related to white matter (WM) structure and is thus an indicator for changes associated with degenerative WM diseases. Estimation of DA is also a necessary precursor to fiber tract mapping (FTM), which holds the potential for understanding brain connectivity. In DTI, signal variations as a function of encoding direction are related to the local tissue diffusion (LTD) from which DA is estimated. In standard DTI, DA is based upon a simplified LTD model requiring only a few encoding directions. But recent work has shown that LTD complexity may not be well fit by the standard model and high angular resolution diffusion (HARD) and multiple diffusion weightings (q-space) are needed to better characterize LTD. This poses three major problems: 1) Analysis and display of HARD and q-space data, and DA and FTM results, is complicated; 2) FTM with HARD data is no longer possible by simple "streamline" methods, and 3) Quantitation of errors in DA and FTM methods is difficult. We have recently developed a DTI analysis plugin to the program AFNI (Analysis of Functional Neurolmages). The primary goal of the current proposal is to further develop this software to address these problems to provide a DTI analysis and visualization platform accessible to all neuroscientists. Our first specific aim is to fully integrate, document, and "productize" our plugin, for dissemination in the AFNI distribution. Also, we have generalized DTI to the spherical harmonic decomposition (SHD), to incorporate more complex LTD models. The second specific aim is to develop rapid, accurate computation and display of the SHD, and our extension to q-space, the spherical wave decomposition (SWD), to produce more accurate DA maps. Our third specific aim is to develop FTM routines based on partial differential equation (PDE) diffusion equation solutions, augmented with prior information, with user-defined "seed" locations, and display within high-resolution anatomical data using advanced visualization methods. The fourth specific aim is to quantitate and display errors in DA and FTM. Motivated by our ongoing work showing changes in WM DA in alcohol abuse patients, and our recent work in fetal alcohol syndrome, Alzheimer's, and language disorders, our ultimate long term goal is to produce a quantitative DTI analysis and visualization tool to aid neuroscientists in their ongoing assessment of degenerate white matter diseases.

描述（由申请人提供）：弥散张量成像（DTI）测量的脑弥散各向异性（DA）与白质（WM）结构有关，因此是与退行性WM疾病相关的变化指标。DA的估计也是纤维束映射（FTM）的必要前提，FTM具有理解大脑连接的潜力。在DTI中，信号的变化作为编码方向的函数与局部组织扩散（LTD）有关，而DA是由局部组织扩散估计的。在标准DTI中，数据处理基于简化的LTD模型，只需要几个编码方向。但最近的研究表明，标准模型可能无法很好地拟合有限责任公司的复杂性，需要高角分辨率扩散（HARD）和多重扩散权重（q空间）来更好地表征有限责任公司。这带来了三个主要问题：1)HARD和q空间数据以及DA和FTM结果的分析和显示非常复杂；2)用简单的“流线”方法对硬数据进行FTM已不可能；3)数据分析和FTM方法的误差难以量化。我们最近为AFNI（功能神经图像分析）程序开发了一个DTI分析插件。当前提案的主要目标是进一步开发该软件来解决这些问题，为所有神经科学家提供一个DTI分析和可视化平台。我们的第一个具体目标是完全集成、文档化和“产品化”我们的插件，以便在AFNI发行版中传播。此外，我们将DTI推广到球面谐波分解（SHD），以纳入更复杂的有限责任公司模型。第二个具体目标是开发快速，准确的计算和显示SHD，以及我们在q空间的扩展，球面波分解（SWD），以产生更精确的DA地图。我们的第三个具体目标是开发基于偏微分方程（PDE）扩散方程解的FTM例程，增强先验信息，用户定义的“种子”位置，并使用先进的可视化方法在高分辨率解剖数据中显示。第四个具体目标是量化和显示DA和FTM中的误差。我们正在进行的研究显示了酒精滥用患者WM DA的变化，以及我们最近在胎儿酒精综合征、阿尔茨海默氏症和语言障碍方面的研究，我们最终的长期目标是开发一种定量的DTI分析和可视化工具，以帮助神经科学家对退行性白质疾病进行持续评估。