Robert L. Barry1,2, Stephen C. Strother3,4,
John C. Gore1,2
1Vanderbilt University
Institute of Imaging Science, Nashville, TN, United States; 2Department
of Radiology & Radiological Sciences, Vanderbilt University Medical
Center, Nashville, TN, United States; 3Rotman Research Institute,
Baycrest, Toronto, ON, Canada; 4Department of Medical Biophysics,
University of Toronto, Toronto, ON, Canada
A challenge with ultra-high-field fMRI is the predominance of noise associated with physiological processes unrelated to tasks of interest. This degradation in data quality may be reversed using post-acquisition algorithms designed to estimate and remove the effects of these noise sources. BOLD fMRI data acquired using 2D EPI and 3D PRESTO at 7T were processed using the Stockwell transform filter, retrospective image correction (RETROICOR), and phase regression. Data quality was evaluated via metrics of prediction and reproducibility using NPAIRS. Results demonstrate the pseudo-complementation of these algorithms and maximization of prediction and reproducibility through synergistic interactions between RETROICOR and phase regression.