Keywords: Machine Learning/Artificial Intelligence, Artifacts, Banding artifacts, Flow artifacts
bSSFP cine imaging suffers from banding and flow artifacts in the region of off-resonance. Suppressing one kind of artifacts may evoke the other kind. For example, phase cycling suppresses banding artifacts, yet its acquisition at multiple frequency offsets often evokes flow artifacts. Here, we develop a partially interpretable neural network for jointly suppressing banding and flow artifacts without phase cycling. Based on a single cine image, the method generates an artifact-corrected image and a voxel-identity map, which guides the artifact suppression and improves its interpretability. Preliminary investigation shows that the method reduces banding and flow artifacts without introducing new artifacts.
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