Imaging Water Diffusion in the Brain and in Other Soft T

我们正在继续开发弥散张量磁共振成像（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；该信息包括局部纤维束方向、水分子在任何给定方向上扩散的均方距离、方向平均平均扩散率以及其他独立于实验室坐标系的标量不变量。这些标量参数是组织的固有特性，但无需造影剂或染料即可测量。例如，一个 DT-MRI 参数，即方向平均扩散率（或迹线），是迄今为止用于可视化正在发生的急性中风的最成功的 MRI 参数。此外，我们已经证明 DT-MRI 可以有效识别通常与慢性中风相关的沃勒变性。对小猫的研究表明，DT-MRI 可用于追踪皮质灰质和白质的早期发育变化，而使用其他方法无法检测到这些变化。 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空间方向不同的解剖白质通路。人脑的彩色图清楚地显示了主要的关联、投射和连合白质通路。他们还允许对大脑的结构解剖学进行详细的研究，这在以前只能使用费力的、侵入性的组织学方法才能实现。为了评估大脑不同功能区域之间的解剖连接性，我们还提出并演示了一种使用 DT-MRI 数据追踪神经纤维束轨迹的方法，我们将其称为 DT-MRI“纤维束成像”。这一发展得益于 Sinisa Pajevic 和 Akram Aldroubi 的贡献，他们实现了一个通用数学框架，以获得对测量的离散、噪声、扩散张量场数据的连续、平滑的近似。我们还开发了非参数（引导）方法，用于根据实验 DT-MRI 数据确定扩散张量统计分布的特征。这些进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方法问题，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 一直在开发扭曲和配准扩散加权图像以及来自不同对象的 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。另一项创新是“张量变量”高斯分布的开发，它充分描述了理想化实验中扩散张量的变异性，可用于改进 DT-MRI 实验的设计和效率。 我们一直在开发更复杂的组织中水扩散的数学模型，并开始使用它们从 MRI 数据推断有关组织（主要是大脑中的白质）的额外微观结构信息。复合受阻和限制扩散模型 (CHARMED) 框架就是这样的一个例子。物理模型也正在开发中，以测试和询问我们的水扩散数学模型。

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.