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

ABSTRACT: Temporal signal-to-noise ratio (tSNR) is a key metric for assessing the ability to detect brain activation in fMRI data. A recent study has shown substantial variation of tSNR between multiple runs of accelerated EPI acquisitions reconstructed with the GRAPPA method using protocols commonly used for fMRI experiments. Across-run changes in the location of high-tSNR regions could lead to misinterpretation of the observed brain activation patterns, reduced sensitivity of the fMRI studies, and biased results. We compared conventional EPI autocalibration (ACS) methods with the recently-introduced FLEET ACS method, measuring their tSNR variability, as well as spatial overlap and displacement of high-tSNR clusters across runs in datasets acquired from human subjects at 7T and 3T. FLEET ACS recons

SUBMITTER: Blazejewska AI 

PROVIDER: S-EPMC5432429 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature