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      Human Connectome Project-style resting-state functional MRI at 7 Tesla using radiofrequency parallel transmission

      , , , , , ,
      NeuroImage
      Elsevier BV

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          Abstract

          We investigate the utility of RF parallel transmission (pTx) for whole-brain resting-state functional MRI (rfMRI) acquisition at 7 Tesla (7T). To this end, Human Connectome Project (HCP)-style data acquisitions were chosen as a showcase example. Five healthy subjects were scanned in pTx and single-channel transmit (1Tx) modes. The pTx data were acquired using a prototype 16-channel transmit system and a commercially available Nova 8-channel transmit 32-channel receive RF head coil. Additionally, pTx single-spoke multiband (MB) pulses were designed to image sagittal slices. HCP-style 7T rfMRI data (1.6-mm isotropic resolution, 5-fold slice and 2-fold inplane acceleration, 3600 volumes and ~ 1-hour scan) were acquired with pTx and the results were compared to those acquired with the original 7T HCP rfMRI protocol. The use of pTx significantly improved flip-angle uniformity across the brain, with coefficient of variation (i.e., std/mean) of whole-brain flip-angle distribution reduced on average by ~39%. This in turn yielded ~17% increase in group temporal SNR (tSNR) as averaged across the entire brain and ~10% increase in group functional contrast-to-noise ratio (fCNR) as averaged across the grayordinate space (including cortical surfaces and subcortical voxels). Furthermore, when placing a seed in either the posterior parietal lobe or putamen estimate seed-based dense connectome, the increase in fCNR was observed to translate into stronger correlation of the seed with the rest of the grayordinate space. We have demonstrated the utility of pTx for slice-accelerated highresolution whole-brain rfMRI at 7T; as compared to current state-of-the-art, the use of pTx improves flip-angle uniformity, increases tSNR, enhances fCNR and strengthens functional connectivity estimation.

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          Author and article information

          Journal
          NeuroImage
          NeuroImage
          Elsevier BV
          10538119
          January 2019
          January 2019
          : 184
          : 396-408
          Article
          10.1016/j.neuroimage.2018.09.038
          6230486
          30237033
          d519e12c-d11e-4805-9a8b-2ba90706fa9f
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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