Echo planar imaging (EPI) requires the correction of susceptibility artifacts for further quantitative analyses. Images acquired in reversed phase-encode (PE) directions are typically used to estimate the susceptibility-induced field from EPI data. In this work, an unsupervised deep Forward-Distortion Network (FD-Net) is proposed for the correction of susceptibility artifacts in EPI: The field and underlying anatomically-correct image are predicted, subject to the constraint that forward-distortion of this image with the field explains the input warped images. This approach provides rapid correction of susceptibility artifacts, with superior performance over deep learning methods that unwarp input images based on a predicted field.
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