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Paper   IPM / Cognitive Sciences / 16370
School of Cognitive Sciences
  Title:   SSFP fMRI at 3 tesla: Efficiency of polar acquisition-reconstruction technique
1.  V. Malekian
2.  F. Rastegar
3.  B. Shafieizargar
4.  A. Nasiraei-Moghaddam
  Status:   Published
  Journal: Magnetic Resonance Imaging
  Vol.:  74
  Year:  2020
  Pages:   1-10
  Supported by:  IPM
SSFP-based fMRI techniques, known for their high specificity and low geometrical distortion, look promising for high-resolution brain mapping. Nevertheless, they suffer from lack of speed and sensitivity, leading them to be exploited mostly in high-field scanners. Radial acquisition can help with these inefficiencies through better tSNR and more effective coverage of the spatial frequencies. Here, we present a SSFP-fMRI approach and experimentally investigate it at 3 T scanners using radial readout for acquisition. In particular, the visual activity is mapped through three bSSFP techniques: 1- Cartesian, 2- Radial with re-gridding reconstruction, 3- Radial with Polar Fourier Transform (PFT) reconstruction. In the PFT technique streaking artifacts, generated at high acceleration rates by re-gridding reconstruction, are avoided and pixel size in the final framework is retrospectively selectable. General agreement, but better tSNR of Radial reading, was first confirmed for these techniques in detection of neural activities at 2 �? 2 mm2 in-plane resolution for all 28 subjects,. Next the outcome of the PFT algorithm with 1 �? 1 mm2 pixel size was compared to images reconstructed by re-gridding (from the same raw data) with the identical pixel size through interpolation. The localization of the activity showed improvement in PFT over interpolation both qualitatively (i.e., well-fitting in gray-matter) and quantitatively (i.e., higher z-scores and tSNR). The proposed technique can therefore be considered as a remedy for lack of speed and sensitivity in SSFP-based fMRI, in conventional field strengths. The proposed approach is particularly useful in task-based studies when we concentrate on a ROI considerably smaller than FOV, without sacrificing spatial resolution.

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