Abstract #1106

Improved high-resolution fMRI image quality with simultaneous multislice VFA-FLEET using a novel multi-kernel slice-GRAPPA algorithm

Avery JL Berman^1,2, Kawin Setsompop^3,4, Thomas Witzel⁵, William A Grissom^6,7, and Jonathan R Polimeni^1,2,8

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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