Imaging Water Diffusion in the Brain and in Other Soft T

大脑和其他软 T 中水扩散的成像

• 批准号: 6991174
• 负责人: PETER J. BASSER
• 金额: --
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6991174
• 关键词: bioimaging /biomedical imaging; body water; brain disorder diagnosis; brain imaging /visualization /scanning; brain mapping; brain morphology; clinical research; diffusion; human subject; magnetic resonance imaging; mathematical model; model design /development; noninvasive diagnosis; statistics /biometry; technology /technique development

We are continuing to develop Diffusion Tensor Magnetic Resonance Imaging (DT-MRI or DTI) as a means to probe tissue microstructure and to assess and diagnose neurological and developmental disorders in vivo. DT-MRI measures a diffusion tensor of water within tissue noninvasively. It consists of relating an effective diffusion tensor to the measured MR spin echo signal; estimating an effective diffusion tensor, D, in each pixel from a set of diffusion-weighted MR images; and calculating and displaying information derived from D. This information includes the local fiber-tract orientation, the mean-squared distance water molecules diffuse in any given direction, the orientationally-averaged mean diffusivity, and other scalar invariant quantities that are independent of the laboratory coordinate system. These scalar parameters are intrinsic properties of the tissue, but are measured without requiring contrast agents or dyes. For example, one DT-MRI parameter, the orientationally-averaged diffusivity (or Trace), has been the most successful MRI parameter used to date to visualize an acute stroke in progress. Moreover, we have shown that DT-MRI is effective in identifying Wallerian degeneration often associated with chronic stroke. Studies with kittens have shown DT-MRI to be useful in following early developmental changes occurring in cortical gray and white matter, which are not detectable using other means. The development of a method to color-encode nerve fiber orientation in the brain by Sinisa Pajevic and Carlo Pierpaoli has allowed us to identify and differentiate anatomical white matter pathways that have similar structure and composition, but different spatial orientations. Color maps of the human brain clearly show the main association, projection, and commissural white matter pathways. They have also allowed detailed studies of the brain's structural anatomy to be performed, which was only possible previously using laborious, invasive histological methods. To assess anatomical connectivity between different functional regions in the brain, we also proposed and demonstrated a way to use DT-MRI data to trace out nerve fiber tract trajectories, which we called DT-MRI "tractography". This development was made possible by contributions by Sinisa Pajevic and Akram Aldroubi who implemented a general mathematical framework for obtaining a continuous, smooth approximation to the measured discrete, noisy, diffusion tensor field data. We have also developed non-parametric (bootstrap) methods for determining features of the statistical distribution of the diffusion tensor from experimental DT-MRI data. These developments have allowed us to apply powerful hypothesis tests to address a wide variety of important biological and clinical questions that previously could only be tackled using ad hoc methods. We are currently addressing other key methodological issues that will enable us to perform quantitative longitudinal and multi-center DT-MRI studies. In particular, Gustavo Rohde has been developing methods to warp and register diffusion weighted images, and DT-MR images from different subjects. Collectively, these developments are enhancing the utility and broadening the scope of the clinical and research applications of DT-MRI. Another innovation has been the development of a "tensor variate" Gaussian distribution that fully describes the variability of the diffusion tensor in an idealized experiment, and can be used to improve the design and efficiency of DT-MRI experiments. We have been developing more sophisticate mathematical models of water diffusion in tissues and have begun using them to infer additional microstructural information about tissue (primarily white matter in the brain) from MRI data. The composite hindered and restricted model of diffusion (CHARMED) framework is one such example. Physical phantoms are also being developed to test and interrogate our mathematical models water diffusion.

我们将继续开发扩散张量磁共振成像（DT-MRI或DTI），作为探测组织微观结构并评估和诊断体内神经和发育障碍的一种手段。 DT-MRI无创的组织内水的扩散张量。它包括将有效的扩散张量与测得的MR自旋回波信号相关。从一组扩散加权MR图像中估算每个像素中的有效扩散张量D；并计算和显示从D。此信息包括局部纤维取向，均值距离水分子在任何给定方向上扩散，定向平均的平均扩散率以及其他与实验室坐标系统无关的标量不变量。这些标量参数是组织的内在特性，但无需对比剂或染料而进行测量。例如，一个DT-MRI参数是定向平均扩散率（或轨迹），它是用于可视化正在进行的急性中风的最成功的MRI参数。此外，我们已经表明，DT-MRI有效地识别通常与慢性中风有关的沃勒式变性。使用小猫的研究表明，DT-MRI可用于遵循皮质灰质和白质发生的早期发育变化，使用其他方法无法检测到这些变化。 Sinisa Pajevic和Carlo Pierpaoli在大脑中染色神经纤维取向的方法的发展使我们能够识别和区分具有相似结构和组成但空间不同的结构和组成的解剖学白质途径。人脑的颜色图清楚地显示了主要的关联，投影和合并白质途径。他们还允许对大脑的结构解剖结构进行详细研究，这只有先前使用费力的侵入性组织学方法才有可能。为了评估大脑不同功能区域之间的解剖连通性，我们还提出并展示了一种使用DT-MRI数据来追踪神经纤维道轨迹的方法，我们称DT-MRI为“拖拉仪”。 Sinisa Pajevic和Akram Aldroubi的贡献使这一发展成为可能，他们实施了一个通用的数学框架，以获得对测得的离散，嘈杂的，嘈杂的，扩散的张量字段数据的连续，平稳的近似。我们还开发了非参数（Bootstrap）方法，用于确定从实验DT-MRI数据中扩散张量的统计分布的特征。这些发展使我们能够应用强大的假设检验来解决各种重要的生物学和临床问题，这些问题只能使用临时方法来解决。我们目前正在解决其他关键的方法论问题，这将使我们能够进行定量纵向和多中心DT-MRI研究。特别是，古斯塔沃·罗德（Gustavo Rohde）一直在开发扭曲和注册扩散加权图像的方法，以及来自不同受试者的DT-MR图像。总的来说，这些发展正在增强效用，并扩大了DT-MRI的临床和研究应用范围。另一个创新是开发“张量变化”高斯分布，该分布完全描述了理想化的实验中扩散张量的可变性，并且可用于提高DT-MRI实验的设计和效率。 我们一直在开发组织中水扩散的更复杂的数学模型，并开始使用它们从MRI数据中推断出有关组织（主要是白质）的其他微观结构信息。复合阻碍和受限的扩散模型（Charmed）框架就是这样的一个例子。还开发了物理幻影来测试和询问我们的数学模型水扩散。