DIIFFUS COEFFICIE IN CAT BRAIN AT 94 T THE INVER LAPLACE TRANSFORM

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在这项工作中，研究了拉普拉斯逆变换(ILT)在表征大脑扩散过程中的作用。该方法已经在9.4T高分辨率采集的体模和活体猫脑数据上实现，并与相同数据的单指数和双指数分析结果进行了比较。结果表明，在白质轴索限制扩散的情况下，所得到的扩散图符合双指数模型。然而，在灰质中，非单指数衰减不会导致ILT中的双峰分布，即使数据可以符合双指数分布。这一发现表明，扩散系数的分布是可能的，而不是离散的双指数行为。结果表明，这种分布对扩散时间等实验参数很敏感。因此，ILT为分析非均质扩散提供了一种独特的工具，即分析扩散系数分布，它具有在组织结构表征中作为有价值的参数的尚未开发的潜力。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In this work, the usefulness of the inverse Laplace transformation (ILT) in the characterization of diffusion processes in the brain has been investigated. The method has been implemented on both phantom and in vivo cat brain data acquired at high resolution at 9.4 T. The results were compared with monoexponential and biexponential analyses of the same data. It is shown that in the case of diffusion restricted by white matter axonal tracts, the resulting diffusograms are in good agreement with the biexponential model. In gray matter, however, the non-monoexponential decay does not lead to a bimodal distribution in the ILT, even though the data can be fitted to a biexponential. This finding suggests the possibility of a distribution of diffusion coefficients rather than a discrete biexponential behavior. It is shown that this distribution is sensitive, for example, to experimental parameters such as the diffusion time. Thus, the ILT offers the possibility of implementing a unique tool for the analysis of heterogeneous diffusion, that is, the analysis of the diffusion coefficient distribution, which has the yet unexplored potential of being a valuable parameter in the characterization of tissue structure.