Automatic cortical surface reconstruction of high-resolution T1 echo planar imaging data

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Massachusetts General Hospital
  • Harvard Medical School
  • Massachusetts Institute of Technology

Abstract

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 1 mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7 T, 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.

Details

Original languageEnglish
Pages (from-to)338-354
Number of pages17
JournalNeuroImage
Volume134
Publication statusPublished - 1 Jul 2016
MoE publication typeA1 Journal article-refereed

    Research areas

  • FMRI registration, FreeSurfer, Functional MRI, Inversion recovery, Surface-based analysis, Tissue segmentation

ID: 3052376