We introduce a deep learning method, named S-Net, to correct the susceptibility artifacts in a pair of reversed phase-encoding (PE) echo-planar imaging images. The S-Net is trained in an unsupervised manner using a set of reversed-PE pairs. For a new reversed-PE pair, the corrected images are computed rapidly by evaluating the learned S-Net. Evaluation of three datasets demonstrates equally good correction performance as much lower computation time (1-3s) than state-of-the-art SAC methods such as AISAC (50-60s) or TOPUP (over 1000s). This fast performance provides a dramatic speedup for medical imaging processing pipelines and makes the real-time correction for MR-scanners feasible.
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