Abstract
The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to study brain function. However, fMRI suffers from susceptibility-induced off resonance fields which may cause geometric distortions and mismatches with anatomical images. State-of-the-art correction methods require acquiring reverse phase encoded images or additional field maps to enable distortion correction. However, not all imaging protocols include these additional scans and thus cannot take advantage of these susceptibility correction capabilities. As such, in this study we aim to enable state-of-the-art distortion correction with FSL's topup algorithm of historical and/or limited fMRI data that include only a structural image and single phase encoded fMRI. To do this, we use 3D U-net models to synthesize undistorted fMRI BOLD contrast images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with topup. We evaluate the efficacy of this approach, named SynBOLD-DisCo (synthetic BOLD images for distortion correction), and show that BOLD images corrected using our approach are geometrically more similar to structural images than the distorted BOLD data and are practically equivalent to state-of-the-art correction methods which require reverse phase encoded data. Future directions include additional validation studies, integration with other preprocessing operations, retraining with broader pathologies, and investigating the effects of spin echo versus gradient echo images for training and distortion correction. In summary, we demonstrate SynBOLD-DisCo corrects distortion of fMRI when reverse phase encoding scans or field maps are not available.