Two-Tensor Model Analysis for DTI

DTI 的二张量模型分析

• 批准号: 7148818
• 负责人: SHARON PELED
• 金额: $ 8.75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7148818
• 关键词: animal tissue; bioimaging /biomedical imaging; brain disorder diagnosis; brain imaging /visualization /scanning; brain mapping; computer program /software; computer simulation; diffusion magnetic resonance imaging; human data; mathematical model; neuropathology; phantom model; spinal cord; technology /technique development; three dimensional imaging /topography; white matter

DESCRIPTION (provided by applicant): MR diffusion tensor imaging (DTI) of the brain provides a unique tool for both visualizing directionality, and assessing intactness of white matter fiber tracts in vivo. In white matter tractography, the anisotropic diffusion tensor is used to delineate fiber tract direction, and trace brain connections from voxel to voxel. For quantitative analyses and diagnoses of possible white matter anomalies, parameters derived from the diffusion tensor can be compared between health and disease. Fitting diffusion data from heterogeneous white matter voxels to a single tensor can lead to errors in both the assessment of white matter tract disruption and the computed tract direction. A method is proposed for effectively resolving two tract directions in a subset of voxels that conform to predetermined criteria for containing no more than two tracts. After fitting the single tensor, the underlying constrained two-tensor model has only 4 free parameters. As a result the necessary MR imaging time is drastically reduced, enabling widespread clinical use. In preliminary simulations, as well as in tests on acquired human DTI data, the model appears robust and useful. The principal aim of the research described in this proposal is to rigorously test the performance of the model on simulated data and on data acquired from an imaging phantom containing fiber bundles crossing each other at known angles. The simulated and in-vitro data will also be used in order to optimize the fitting method itself, and to evaluate the sensitivity of the method to noise, and to details of the tract configuration such as angle separation and relative size. The criteria defining for which voxels this model is suitable will be investigated. A variety of diseases, in which white matter injury is known or hypothesized, have been investigated with DTI (multiple sclerosis, schizophrenia, amyotrophic lateral sclerosis, Alzheimer's disease, brain tumors, and more). Using tractography it is possible to evaluate white matter integrity and disruption of connectivity in disease by focusing on important connections between different brain areas. To perform tractography effectively there exists a need to identify areas of crossing tracts, and to find the directions of the different tracts. The method proposed herein optimizes the information available from DTI acquired in clinically feasible time frames.

描述（由申请人提供）：大脑的扩散张量成像（DTI）MR为可视化方向性和评估体内白质纤维的完整性提供了独特的工具。在白质拖拉机中，各向异性扩散张量用于描绘纤维道的方向，并跟踪从体素到体素的脑连接。为了进行定量分析和诊断可能的白质异常，可以在健康和疾病之间比较从扩散张量的参数。从异质白质体素到单个张量的拟合扩散数据可能会导致对白质道破坏和计算的道方向的评估中的错误。提出了一种方法，以有效地在一个符合预定标准的体素中有效解析两个区域的两个方向。拟合单个张量后，基础约束的两张量模型只有4个自由参数。结果，必要的MR成像时间大大减少，从而实现了广泛的临床用途。在初步模拟以及对获得的人DTI数据的测试中，该模型显得稳健且有用。本提案中描述的研究的主要目的是严格测试模型在模拟数据上的性能以及从包含纤维束的成像幻影获得的数据，以已知角度相互交叉。还将使用模拟和视频数据数据，以优化拟合方法本身，并评估该方法对噪声的敏感性，以及范围构型的细节，例如角度分离和相对大小。将研究该模型的体素定义的标准。已通过DTI（多发性硬化症，精神分裂症，肌萎缩性侧面硬化症，阿尔茨海默氏病，脑肿瘤等）研究了多种已知或假设白质损伤的疾病。使用拖拉机可以通过关注不同大脑区域之间的重要连接来评估疾病中的白质完整性和连通性的破坏。为了有效地执行拖拉术，需要识别交叉区域的区域，并找到不同道的方向。本文提出的方法优化了在临床上可行的时间范围内获得的DTI可用信息。