Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
Neuroimage. 2011 Jan 15;54(2):974-84. doi: 10.1016/j.neuroimage.2010.09.008. Epub 2010 Sep 19.
Development of a diffusion tensor (DT) template that is representative of the micro-architecture of the human brain is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the generation of a detailed white matter atlas. Furthermore, a DT template in ICBM space may simplify consolidation of information from DT, anatomical and functional MRI studies. The previously developed "IIT DT brain template" was produced in ICBM-152 space, based on a large number of subjects from a limited age-range, using data with minimal image artifacts, and non-linear registration. That template was characterized by higher image sharpness, provided the ability to distinguish smaller white matter fiber structures, and contained fewer image artifacts, than several previously published DT templates. However, low-dimensional registration was used in the development of that template, which led to a mismatch of DT information across subjects, eventually manifested as loss of local diffusion information and errors in the final tensors. Also, low-dimensional registration led to a mismatch of the anatomy in the IIT and ICBM-152 templates. In this work, a significantly improved DT brain template in ICBM-152 space was developed, using high-dimensional non-linear registration and the raw data collected for the purposes of the IIT template. The accuracy of inter-subject DT matching was significantly increased compared to that achieved for the development of the IIT template. Consequently, the new template contained DT information that was more representative of single-subject human brain data, and was characterized by higher image sharpness than the IIT template. Furthermore, a bootstrap approach demonstrated that the variance of tensor characteristics was lower in the new template. Additionally, compared to the IIT template, brain anatomy in the new template more accurately matched ICBM-152 space. Finally, spatial normalization of a number of DT datasets through registration to the new and existing IIT templates was improved when using the new template.
开发一个能够代表人类大脑微观结构的弥散张量（DT）模板对于比较不同人群的神经元结构完整性和脑连接性以及生成详细的白质图谱至关重要。此外，在 ICBM 空间中的 DT 模板可能会简化来自 DT、解剖学和功能 MRI 研究的信息的整合。先前开发的“IIT DT 脑模板”是在 ICBM-152 空间中生成的，基于来自有限年龄范围的大量受试者，使用具有最小图像伪影和非线性配准的数据。与几个先前发布的 DT 模板相比，该模板具有更高的图像清晰度，能够区分更小的白质纤维结构，并且图像伪影更少。然而，在该模板的开发中使用了低维配准，这导致了跨受试者的 DT 信息不匹配，最终表现为局部扩散信息的丢失和最终张量中的错误。此外，低维配准导致 IIT 和 ICBM-152 模板中的解剖结构不匹配。在这项工作中，使用高维非线性配准和为 IIT 模板收集的原始数据，在 ICBM-152 空间中开发了一个显著改进的 DT 脑模板。与用于开发 IIT 模板的方法相比，跨受试者的 DT 匹配的准确性大大提高。因此，新模板包含的 DT 信息更能代表单个人类大脑数据，并且比 IIT 模板具有更高的图像清晰度。此外，通过对新模板和现有的 IIT 模板进行配准，对多个 DT 数据集进行空间归一化的效果得到了改善。
