Development of a Brain Template for Diffusion-Tensor MRI

描述（由申请人提供）：开发用于扩散张量成像（DTI）的脑模板对于比较不同人群的神经元结构完整性和脑连接性至关重要。DTI是一种非侵入性技术，可提供有关脑组织微结构特征的独特信息。然而，患者和健康志愿者人群之间的DTI结果的比较主要集中在从扩散张量导出的标量上，忽略了张量中可用的部分信息。其主要原因是无法对DTI数据进行准确的空间归一化，这是比较某些方向相关张量信息的必要步骤。DTI数据的空间归一化的准确性部分地由于常规DTI数据采集基于回波平面成像（EPI）的事实而受到损害，回波平面成像（EPI）遭受失真和图像伪影。此外，为了提高归一化的准确性，在配准过程中必须使用张量的所有信息。然而，大脑结构及其张量的精确匹配需要非线性配准方法，这对张量的噪声敏感。最后，不存在不仅包含解剖特征而且包含具有低噪声内容的DTI信息的大脑模板。所有上述因素降低了DTI数据配准的准确性，并阻止了脑组织某些弥散和结构特征的组间比较，从而限制了DTI的临床潜力。相比之下，Turboprop-DTI是一种成像技术，其提供的DTI数据比基于EPI的DTI具有显著更少的伪影。然而，Turboprop-DTI的特征在于比基于EPI的DTI更慢的数据采集和更高的噪声水平。我们最近介绍了一种迭代图像重建方法的涡轮螺旋桨成像的基础上的非均匀快速傅立叶变换（NUFFT），它可以提高精度和降低噪声水平相比，传统的涡轮螺旋桨重建技术。因此，本项目的广泛目标是：a）开发Turboprop-DTI数据采集策略，结合我们最近引入的图像重建技术，将在临床可接受的时间内提供低噪声含量和最小伪影的数据，B）开发对张量噪声不太敏感的鲁棒配准技术，以便c）为DTI生成准确的大脑模板。这项研究的成功完成将使DTI数据的准确登记和跨人群的结构完整性和大脑连接的全面比较成为可能。因此，这项研究的结果将增强DTI作为诊断工具的作用，为广泛的临床问题。开发用于扩散张量成像（DTI）的大脑模板对于比较不同人群的神经元结构完整性和大脑连接性至关重要。该项目的主要目标是开发强大的注册技术，并为DTI生成准确的大脑模板。这项研究的成功完成将允许对不同人群的结构完整性和大脑连接性进行全面比较，并将增强DTI作为广泛临床问题诊断工具的作用。

DESCRIPTION (provided by applicant): The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations. DTI is a non-invasive technique that provides unique information regarding the microstructural characteristics of brain tissue. However, comparisons of DTI results between populations of patients and healthy volunteers have primarily focused only on scalar quantities derived from the diffusion tensor, overlooking portion of the information available in the tensor. The primary reason for this is the inability to perform accurate spatial normalization of DTI data, which is a necessary step for the comparison of certain orientation-dependent tensor information. The accuracy of spatial normalization of DTI data is compromised partly due to the fact that conventional DTI data acquisitions are based on echo- planar imaging (EPI), which suffers from distortions and image artifacts. Furthermore, to increase the accuracy of normalization, all the information of the tensor must be used in the registration process. However, accurate matching of brain structures and their tensors requires non-linear registration methods, which are sensitive to the tensors' noise. Finally, a brain template that contains not only anatomical features but also DTI information with low noise content does not exist. All of the above factors reduce the accuracy in registration of DTI data and prevent intergroup comparisons of certain diffusion and structural characteristics of brain tissue, thus limiting the clinical potential of DTI. In contrast, Turboprop-DTI is an imaging technique that provides DTI data with significantly fewer artifacts than EPI-based DTI. However, Turboprop-DTI is characterized by slower data acquisition and higher noise-levels than EPI-based DTI. We recently introduced an iterative image reconstruction method for Turboprop imaging based on the non-uniform fast Fourier transform (NUFFT), which can increase accuracy and reduce noise levels compared to conventional Turboprop reconstruction techniques. Therefore, the broad objectives of this project are: a) to develop Turboprop-DTI data acquisition strategies that, in combination with our recently introduced image reconstruction technique, will provide data with low noise content and minimal artifacts in a clinically acceptable time, b) to develop robust registration techniques that are less sensitive to the tensors' noise, in order to c) produce an accurate brain template for DTI. The successful completion of this research will allow for accurate registration of DTI data and for comprehensive comparisons of structural integrity and brain connectivity across populations. Therefore, the results of this research will enhance the role of DTI as a diagnostic tool for a wide range of clinical problems. PUBLIC HEALTH RELEVANCE The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations. The broad objective of this project is to develop robust registration techniques and produce an accurate brain template for DTI. The successful completion of this research will allow for comprehensive comparisons of structural integrity and brain connectivity across populations, and will enhance the role of DTI as a diagnostic tool for a wide range of clinical problems.