Abstract #2701

Evaluating data precision and signal gains in functional neuroimaging data after NOise reduction with DIstribution Corrected PCA (NORDIC)

Logan T. Dowdle¹, Luca Vizioli^2,3, Steen Moeller², Cheryl Olman⁴, Geoffrey Ghose^1,2,4, Essa Yacoub², and Kamil Ugurbil^1,2,5

¹Neurosciences, Center for Magnetic Resonance Research, Minneapolis, MN, United States, ²Radiology, Center for Magnetic Resonance Research, Minneapolis, MN, United States, ³Neurosurgery, University of Minnesota, Minneapolis, MN, United States, ⁴Psychology, University of Minnesota, Minneapolis, MN, United States, ⁵Medicine, Center for Magnetic Resonance Research, Minneapolis, MN, United States

Functional neuroimaging with gradient echo BOLD has moved to higher and higher spatial and temporal resolutions, which leads to with higher levels of unstructured thermal noise. Here we evaluate the application of the NORDIC method, which suppresses thermal noise, on 7 datasets. We find that NORDIC improve t-values, while not significantly altering fMRI signal characteristics. In addition, a single run of NORDIC data is able to predict held out, original data equally as well as 2 to 3 runs of the original data. NORDIC is a promising new method for dramatically increasing the signal to noise ratio of BOLD imaging.

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