Abstract #1106
Improved high-resolution fMRI image quality with simultaneous multislice VFA-FLEET using a novel multi-kernel slice-GRAPPA algorithm
Avery JL Berman1,2, Kawin Setsompop3,4, Thomas Witzel5, William A Grissom6,7, and Jonathan R Polimeni1,2,8
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Radiology, Stanford University, Palo Alto, CA, United States, 4Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States, 5Q Bio, Inc., San Carlos, CA, United States, 6Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, 7Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 8Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
Synopsis
We propose a novel slice-GRAPPA reconstruction algorithm, termed multi-kernel slice-GRAPPA (mks-GRAPPA), to tackle the challenge of reconstructing high spatial resolution segmented multi-shot EPI data for fMRI. This is particularly relevant for the recently proposed Variable-Flip-Angle “FLEET” pulse sequence. For a segmentation factor, S, by training 2×S slice-GRAPPA kernels, rather than one, we demonstrate significant improvements in image quality metrics under a wide range of protocols. The multitude of kernels account for static signal discontinuities within and across segments in multi-shot EPI. In the SNR starved regime of high-res fMRI, mks-GRAPPA allows us to recover a significant portion of lost tSNR.
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