Imaging Water Diffusion in the Brain and in Other Soft T

我们正在继续开发扩散张量磁共振成像(DT-MRI或DTI)，作为一种探测组织微结构以及评估和诊断体内神经和发育障碍的手段。DT-MRI非侵入性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图像估计每个像素中的有效扩散张量D；以及计算和显示从D获得的信息。这些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、方向平均的平均扩散系数以及与实验室坐标系无关的其他标量不变量。这些标量参数是组织的固有属性，但无需对比剂或染料即可测量。例如，一个DT-MRI参数，即方向平均弥散率(或轨迹)，是迄今为止用于可视化急性中风进展情况的最成功的MRI参数。此外，我们已经证明，DT-MRI在识别常与慢性中风相关的沃勒变性方面是有效的。对小猫的研究表明，DT-MRI有助于追踪皮质灰质和白质发生的早期发育变化，而这些变化是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彩色编码的方法，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。人脑的彩色图谱清楚地显示了主要的联系、投射和连合的白质通路。他们还允许对大脑的结构解剖进行详细的研究，这在以前只有使用费力的侵入性组织学方法才能实现。为了评估大脑不同功能区之间的解剖学连通性，我们还提出并演示了一种使用DT-MRI数据来追踪神经纤维束轨迹的方法，我们称之为DT-MRI“纤维束成像”。这一发展归功于Sinisa Pajevic和Akram Aldroubi的贡献，他们实现了一个通用的数学框架，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(自举)方法，用于从实验DT-MRI数据中确定扩散张量的统计分布特征。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，而这些问题以前只能使用特别的方法来解决。我们目前正在解决其他关键的方法学问题，这些问题将使我们能够进行量化、纵向和多中心的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和来自不同对象的DT-MR图像的方法。总体而言，这些发展正在增强DT-MRI的实用性，并拓宽其临床和研究应用的范围。另一项创新是发展了一种“张量变量”高斯分布，它完全描述了理想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。 我们一直在开发更复杂的水在组织中扩散的数学模型，并开始使用它们从MRI数据中推断关于组织(主要是大脑中的白质)的额外微结构信息。扩散的复合阻碍和限制模型(CHAMED)框架就是这样一个例子。物理模型也在开发中，用来测试和检验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.