MRI+DTI-Based Tools for Analyzing White Matter Variation

基于 MRI DTI 的白质变化分析工具

• 批准号: 6915946
• 负责人: DAVID H LAIDLAW
• 金额: $ 48.13万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-23 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6915946
• 关键词: Macaca mulatta; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain mapping; clinical research; computer program /software; computer system design /evaluation; diffusion magnetic resonance imaging; human subject; mathematical model; white matter

DESCRIPTION (provided by applicant): In this multidisciplinary project, a team of investigators will design and apply software tools that can simultaneously segment neural tissues and identify the locations of bundles of neural fibers in the brain. The tools will operate on combined structural and diffusion magnetic resonance (MR) datasets of the nervous system and will produce morphometric measures of each white matter (WM) structure, including its trajectory; cross section, which may vary along the trajectory; and fiber density. The proposed software tools will globally model imaging datasets. Numerical algorithms will adjust the parameters of a model of neural tissues and WM structures until the model is consistent with all of the acquired imaging data and maintains anatomical constraints such as incompressibility and continuity of neural fibers. The tools will differ from current morphometric tools in several ways: they will be more automated; they will incorporate and use all of the complementary information available in the different MR modalities; and they will not have the inaccuracies that are inherent to most current tractography methods. This research project is innovative in several ways. First, the WM measures will be comparable across subjects without image-level registration because parameters based on WM structures can be compared directly. Second, the investigators will use inverse solution methods to model the multi-valued volume images, globally resolve ambiguities in morphometric measures from local image artifacts such as partial volume effects. Third, the modeling approach will not contract diffusion measurements to tensor values. Thus, hard-to-resolve features such as fiber intersections and projections will be preserved. Finally, we will validate the tools at many levels, including histology, macaque imaging, biological variation in normal volunteers, and clinical feasibility studies in brain tumors, HIV-related neuropathology, and multiple sclerosis. The successful development, validation, and application of these sophisticated software tools may spur further development of medical imaging data modeling. The precise measures of brain structures produced should have a significant impact on biomedical research, will provide a deeper understanding of the brain and how it changes, and could play an important role in surgical planning. More broadly, the tools should apply to research studies of any biological process that involves changes in WM.

描述（由申请人提供）：在这个多学科项目中，研究人员团队将设计和应用软件工具，可以同时分割神经组织并识别大脑中神经纤维束的位置。这些工具将在神经系统的组合结构和扩散磁共振（MR）数据集上运行，并将产生每个白质（WM）结构的形态测量，包括其轨迹；横截面，可能沿轨迹变化；和纤维密度。 所提出的软件工具将对成像数据集进行全局建模。数值算法将调整神经组织和 WM 结构模型的参数，直到该模型与所有采集的成像数据一致并保持解剖学约束，例如神经纤维的不可压缩性和连续性。这些工具将在几个方面与当前的形态测量工具有所不同：它们将更加自动化；他们将整合并使用不同 MR 模式中可用的所有补充信息；而且它们不会出现大多数当前纤维束成像方法所固有的不准确性。 该研究项目在多个方面具有创新性。首先，WM 测量将在没有图像级配准的情况下在受试者之间进行比较，因为可以直接比较基于 WM 结构的参数。其次，研究人员将使用逆解方法对多值体积图像进行建模，全局解决局部图像伪影（例如部分体积效应）在形态测量中的模糊性。第三，建模方法不会将扩散测量收缩为张量值。因此，难以解析的特征（例如纤维交叉点和投影）将被保留。最后，我们将在多个层面上验证这些工具，包括组织学、猕猴成像、正常志愿者的生物变异，以及脑肿瘤、艾滋病毒相关神经病理学和多发性硬化症的临床可行性研究。这些复杂软件工具的成功开发、验证和应用可能会刺激医学成像数据建模的进一步发展。对大脑结构的精确测量将对生物医学研究产生重大影响，将提供对大脑及其变化方式的更深入了解，并可能在手术计划中发挥重要作用。更广泛地说，这些工具应适用于涉及 WM 变化的任何生物过程的研究。