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Abstract #3739

Reduction of run-to-run variability of temporal SNR in accelerated EPI time-series data through FLEET-based robust autocalibration

Anna I. Blazejewska1, Himanshu Bhat2, Lawrence L. Wald1,3, and Jonathan R. Polimeni1

1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States, 2Siemens Medical Solutions USA Inc., Charlestown, MA, United States, 3Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Temporal signal-to-noise ratio (tSNR) provides a crucial metricdetermining sensitivity of the acquisition to BOLD fMRI measurements. Ithas been shown that tSNR may vary dramatically between multiple runs ofaccelerated single-shot EPI acquisitions using single- or multi-shot EPIto acquire autocalibration or ACS data. We applied noise-to-noise ratio(NNR) measure to map run-to-run variability of acquisitions usingconventional multi-shot EPI ACS data as well as recently proposed Fastlow-angle excitation echo-planar technique (FLEET) ACS. tSNR variabilitybetween multiple runs improved in acquisitions using FLEET-ACS, providing the potential to increase sensitivity of BOLD fMRI experiments.

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