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.

我们正在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以及评估和诊断体内神经和发育障碍的手段。DT-MRI无创地测量组织内水的扩散张量。它包括将有效扩散张量与测量的MR自旋回波信号相关联;从一组扩散加权MR图像中估计每个像素中的有效扩散张量D;以及计算和显示从D导出的信息。该信息包括局部纤维束取向、水分子在任何给定方向上扩散的均方距离、取向平均的平均扩散率以及独立于实验室坐标系的其他标量不变量。这些标量参数是组织的固有特性，但是在不需要造影剂或染料的情况下进行测量。例如，一个DT-MRI参数，定向平均扩散率（或迹线），是迄今为止用于可视化进展中的急性卒中的最成功的MRI参数。此外，我们已经表明，DT-MRI是有效的识别沃勒变性往往与慢性中风。对小猫的研究表明，DT-MRI可用于跟踪皮质灰质和白色物质中发生的早期发育变化，这些变化是使用其他方法无法检测到的。Sinisa Pajevic和Carlo Pierpaoli开发了一种对大脑中神经纤维方向进行颜色编码的方法，使我们能够识别和区分具有相似结构和组成但不同空间方向的解剖学白色物质通路。人脑的彩色图清楚地显示了主要的联想、投射和连合白色物质通路。他们还允许对大脑的结构解剖进行详细的研究，这在以前只能使用费力的侵入性组织学方法。为了评估大脑中不同功能区域之间的解剖连接，我们还提出并展示了一种使用DT-MRI数据来描绘神经纤维束轨迹的方法，我们称之为DT-MRI“纤维束成像”。Sinisa Pajevic和Akram Aldroubi的贡献使这一发展成为可能，他们实现了一个通用的数学框架，用于获得对测量的离散，噪声，扩散张量场数据的连续，平滑近似。我们还开发了非参数（自举）的方法来确定实验DT-MRI数据的扩散张量的统计分布的功能。这些发展使我们能够应用强大的假设检验来解决以前只能使用临时方法解决的各种重要的生物学和临床问题。我们目前正在解决其他关键的方法学问题，这将使我们能够进行定量纵向和多中心DT-MRI研究。特别是，Gustavo Rohde一直在开发用于扭曲和配准扩散加权图像和来自不同受试者的DT-MR图像的方法。总的来说，这些发展正在增强DT-MRI的实用性并扩大其临床和研究应用的范围。另一个创新是开发了一种“张量变量”高斯分布，它充分描述了理想化实验中扩散张量的可变性，并可用于改进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精确的数学模型，并已开始使用它们来从MRI数据中推断有关组织（主要是大脑中的白色物质）的额外微观结构信息。复合受阻和限制扩散模型（CHARMED）框架就是这样一个例子。物理幻影也正在开发中，以测试和询问我们的数学模型水扩散。