Imaging Water Diffusion in the Brain and in Other Soft Tissues

我们正在继续开发新的磁共振（MR）为基础的位移成像方法，如扩散张量MRI（DT-MRI或DTI）。DTI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。这些标量参数是我们在不需要造影剂或染料的情况下测量的组织的固有特性。例如，一个DTI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中风的最成功的成像参数。此外，我们已经表明，DTI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，DTI可用于跟踪皮质灰质和白色物质中发生的早期发育变化，这些变化使用其他方法无法检测到。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DTI数据描绘神经纤维束轨迹的方法，我们称之为DTI“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DTI数据的扩散张量的统计分布的功能。另一个创新是张量变量高斯分布的发展，它充分描述了理想化DTI实验中扩散张量的可变性，并可用于改善DTI实验的设计和效率。这些集体发展现在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。我们目前正在解决几个关键的方法学问题，这将使我们能够进行定量纵向和多中心DTI研究。 最近，我们一直在开发组织中水扩散的更复杂的数学模型，并开始使用这些模型从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）框架就是一个例子。我们最近提出的AxCaliber方法是另一种方法。 它使我们能够估计轴突直径分布内的神经束从MR位移成像数据。最近由Michael Komlosh开发的复杂的扩散加权NMR和MRI序列帮助我们表征组织内的微观各向异性，如宏观各向同性的灰质。 她和Ferenc Horkay开发了物理幻影来测试和询问我们在这种复杂组织中水扩散的数学模型。埃夫伦Ozarslan一直在开发新的方法来表征在各种组织标本中观察到的异常扩散。从这些测量中获得的参数可能为有前途的诊断应用（如Brodmann parcellation或癌症检测）提供新的MR对比源。他还开发了新的方法来表征使用MRI测量的位移分布的非高斯特征。 Valery Pikalov正在与STBB成员合作，使用相对少量的扩散加权图像（DWI）重建平均传播算子。 这个量是位移成像的“圣杯”，它可以用来推断微观限制区室的几何特征，以及收集DTI提供的所有信息。 总的来说，这些方法代表了用于进行体内MRI组织学的框架，提供了详细的显微结构和微结构信息，否则只能使用费力的组织学或病理学技术对切除或活检标本获得。

We are continuing to develop novel Magnetic Resonance (MR) based displacement imaging methods, such as Diffusion Tensor MRI (DT-MRI or DTI). DTI measures a diffusion tensor of water within tissue. 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 that we measure without requiring contrast agents or dyes. For example, one DTI parameter, the orientationally-averaged diffusivity (or Trace), has been the most successful imaging parameter used to date to visualize an acute stroke in progress. Moreover, we have shown that DTI is effective in identifying Wallerian degeneration often associated with chronic stroke. Studies with kittens have shown DTI 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 brains 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 DTI data to trace out nerve fiber tract trajectories, which we called DTI "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 DTI data. Another innovation has been the development of a tensor variate Gaussian distribution that fully describes the variability of the diffusion tensor in an idealized DTI experiment, and can be used to improve the design and efficiency of DTI experiments. These collective developments are now allowing 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 several key methodological issues that will enable us to perform quantitative longitudinal and multi-center DTI studies. More recently, we have been developing more sophisticated mathematical models of water diffusion in tissues and have begun using these 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 example. Our recently proposed AxCaliber method is another. It allows us to estimate the axon diameter distribution within a nerve bundle from MR displacement imaging data. Sophisticated diffusion weighted NMR and MRI sequences, recently developed by Michael Komlosh, help us characterize microscopic anisotropy within tissues like gray matter that are macroscopically isotropic. She and Ferenc Horkay have developed physical phantoms to test and interrogate our mathematical models of water diffusion in such complex tissue. Evren Ozarslan has been developing novel ways to characterize anomalous diffusion observed in various tissue specimen. Parameters derived from these measurements may provide a new source of MR contrast for promising diagnostic applications such as Brodmann parcellation or cancer detection. He has also developed novel approaches to characterize non-Gaussian features of the displacement distribution measured using MRI. Valery Pikalov is working with STBB members to reconstruct the average propagator using a relatively small number of diffusion weighted images (DWI). This quantity is the "holy grail" of displacement imaging, which can by used to infer geometric features of microscopic restricted compartments as well as glean all of the information provided by DTI. Collectively, these methods represent a framework for performing in vivo MRI histology, providing detailed microstructural and microarchitectural information that otherwise could only be obtained using laborious histological or pathological techniques on excised or biopsied specimens.