fMRI acquisitions benefit from spiral trajectories; however, their use is commonly restricted due to off-resonance blurring artifacts. This work presents a deep-learning-based model for spiral deblurring in inhomogeneous fields. Training of the model utilized blurred simulated images from interleaved EPI data with various degrees of off-resonance. We investigated the effect of using the field map during training and compared correction performance with the MFI technique. Quantitative validation results demonstrated that the proposed method outperforms MFI for all inhomogeneity scenarios with SSIM>0.97, pSNR>35 dB, and HFEN<0.17. Filter visualization suggests blur learning and mitigation as expected.
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