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. Wald^1,3, 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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