Abstract #1747

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

Ross W. Mair^1,2, R. Matthew Hutchison^1,3, 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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