Renvall Ville, Witzel Thomas, Wald Lawrence L, Polimeni Jonathan R
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Radiology, Harvard Medical School, Boston, MA, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
Neuroimage. 2016 Jul 1;134:338-354. doi: 10.1016/j.neuroimage.2016.04.004. Epub 2016 Apr 11.
Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data.
回波平面成像（EPI）是大多数功能磁共振成像（fMRI）的首选方法，然而EPI容易出现几何失真，因此与传统解剖学参考数据无法对齐。功能数据和解剖学数据之间较差的几何对应关系可能导致检测到的激活模式严重错位和失真。然而，成像技术的最新进展为EPI数据提供了更高的质量和分辨率。在这里，我们提出了一个框架，用于直接从基于高分辨率EPI的参考图像中推导皮质表面重建，这些参考图像提供了与功能数据精确匹配几何失真的解剖模型。使用快速多次反转恢复时间EPI序列（MI-EPI）在7T下采集各向同性体素大小为1mm的解剖学EPI数据，并从中计算定量T1图谱。利用这些T1图谱，使用布洛赫方程合成模仿标准解剖学数据组织对比度的体积数据，并使用FreeSurfer自动处理这些T1加权数据。与传统解剖学参考相比，T2（⁎）加权EPI数据与基于合成T1加权解剖学MI-EPI的图像之间的空间对齐得到了改善。特别是，由于磁敏感性差异而容易出现失真的区域附近的对齐得到了改善，并且当使用直接从MI-EPI参考中推导的皮质表面重建时，皮质外相邻组织类别的采样减少。因此，MI-EPI方法产生了高质量的解剖学数据，可以用标准软件自动分割，提供与BOLD fMRI数据几何匹配的皮质表面重建。
