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Abstract #1747

Optimizing SMS-BOLD image reconstruction for resting state analysis and reconstruction time

Ross W. Mair1,2, R. Matthew Hutchison1,3, Stephanie McMains1, and Steven Cauley2

1Center for Brain Science, Harvard University, Cambridge, MA, United States, 2A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 3Department of Psychology, Harvard University, Cambridge, MA, United States

The computational processes required for slice-unaliasing in SMS-BOLD scans are taxing on the scanner reconstruction computers, so data is sometimes observed far from real-time, and reconstruction may lag up to tens of minutes behind the acquisition. A channel compression algorithm has been proposed to counter computational demands of these reconstruction processes. We studied functional networks derived from resting-state scans as a function of slice acceleration, slice-GRAPPA kernel size and channel compression to find an optimal solution for an existing, conventional 3.0 T scanner. Slice-GRAPPA kernel size played little effect in functional network definition and two-fold channel compression was beneficial to reconstruction time without impacting functional network data quality.

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