Imaging Water Diffusion in the Brain and in Other Soft T

我们正在继续发展扩散张量磁共振成像（DT-MRI或DTI）作为一种手段来探测组织微观结构和评估和诊断神经和发育障碍在体内。DT-MRI无创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磁共振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的有效扩散张量D；计算并显示从d导出的信息。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、方向平均平均扩散率和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属性，但测量时不需要造影剂或染料。例如，一个DT-MRI参数，定向平均扩散率（或Trace），是迄今为止用于观察急性中风进展的最成功的MRI参数。此外，我们已经表明，DT-MRI在识别沃勒氏变性通常与慢性中风是有效的。对小猫的研究表明，DT-MRI在追踪发生在皮质灰质和白质的早期发育变化方面是有用的，这些变化是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结构和组成相似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、投射和连接白质通路。他们还允许对大脑结构解剖进行详细的研究，这在以前只能通过费力的侵入性组织学方法来实现。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，我们称之为DT-MRI“神经束成像”。由于Sinisa Pajevic和Akram Aldroubi的贡献，这一发展成为可能，他们实现了一个通用的数学框架，用于获得测量的离散、噪声、扩散张量场数据的连续、光滑近似。我们还开发了非参数（bootstrap）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各样的重要的生物学和临床问题，而这些问题以前只能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，这将使我们能够进行定量的纵向和多中心DT-MRI研究。Gustavo Rohde一直致力于开发不同主体的扩散加权图像和DT-MR图像的扭曲和配准方法。总的来说，这些发展增强了ct - mri的实用性，拓宽了临床和研究应用的范围。另一项创新是“张量变量”高斯分布的发展，它充分描述了理想实验中扩散张量的可变性，可用于改进DT-MRI实验的设计和效率。

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.