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

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 在这项工作中，有用的逆拉普拉斯变换（ILT）在脑中的扩散过程的表征进行了研究。该方法已实现了在9.4 T高分辨率下获得的体模和在体猫脑数据。将结果与相同数据的单指数和双指数分析进行比较。结果表明，在扩散受白色物质轴突束限制的情况下，所得到的扩散图与双指数模型吻合得很好。然而，在灰质中，非单指数衰减不会导致ILT中的双峰分布，即使数据可以拟合为双指数。这一发现表明，扩散系数的分布，而不是离散的双指数行为的可能性。结果表明，这种分布是敏感的，例如，实验参数，如扩散时间。因此，ILT提供了实施一个独特的工具，用于分析的异质扩散，即，扩散系数分布的分析，这具有尚未开发的潜力，是一个有价值的参数在组织结构的表征的可能性。

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.