Keywords: Image Reconstruction, SusceptibilityAlthough a major advantage of SPEN vs EPI is a higher immunity to artifacts, it suffers from Nyquist or motion artifacts. We proposed a new unsupervised CNN model that takes advantage of both physical model and Deep learning. The model consists of three parts: phase feature extraction module, which can extract the phase features of even/odd phase differences or motion-caused phase differences in multi-shot echo data. Then, the phase maps are generated with these phase difference features. Lastly, the phase correction modules to remove artifacts. The results show that the proposed model can effectively correct Nyquist/motion artifacts in single-shot/multi-shot SPEN.
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