Liu Zhexing, Wang Yi, Gerig Guido, Gouttard Sylvain, Tao Ran, Fletcher Thomas, Styner Martin
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA 27510.
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA 84408.
Proc SPIE Int Soc Opt Eng. 2010 Mar 11;7628:76280J-. doi: 10.1117/12.844748.
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. Currently, routine DTI QC procedures are conducted manually by visually checking the DWI data set in a gradient by gradient and slice by slice way. The results often suffer from low consistence across different data sets, lack of agreement of different experts, and difficulty to judge motion artifacts by qualitative inspection. Additionally considerable manpower is needed for this step due to the large number of images to QC, which is common for group comparison and longitudinal studies, especially with increasing number of diffusion gradient directions. We present a framework for automatic DWI QC. We developed a tool called DTIPrep which pipelines the QC steps with a detailed protocoling and reporting facility. And it is fully open source. This framework/tool has been successfully applied to several DTI studies with several hundred DWIs in our lab as well as collaborating labs in Utah and Iowa. In our studies, the tool provides a crucial piece for robust DTI analysis in brain white matter study.
扩散张量成像（DTI）已成为一种重要的磁共振成像（MRI）检查方法，用于在活体状态下研究脑白质的完整性。DTI是根据一系列采集的扩散加权成像（DWI）容积数据估算得出的。DWI数据存在固有的低信噪比问题，多方向编码的整体扫描时间较长，相应地遭遇多种伪影的风险也较大。这些伪影可能过于严重，以至于无法正确、稳定地估算扩散张量。因此，对于DTI研究而言，质量控制（QC）程序绝对必要。目前，常规的DTI质量控制程序是通过逐梯度、逐切片地目视检查DWI数据集来手动进行的。结果往往在不同数据集之间一致性较低，不同专家之间缺乏共识，并且通过定性检查难以判断运动伪影。此外，由于需要质量控制的图像数量众多，这一步骤需要大量人力，这在组间比较和纵向研究中很常见，尤其是随着扩散梯度方向数量的增加。我们提出了一个用于自动DWI质量控制的框架。我们开发了一个名为DTIPrep的工具，它通过详细的流程规划和报告功能将质量控制步骤整合在一起。并且它是完全开源的。该框架/工具已成功应用于我们实验室以及犹他州和爱荷华州的合作实验室的多项DTI研究中，涉及数百个DWI数据。在我们的研究中，该工具为脑白质研究中的稳健DTI分析提供了关键支持。
