Reconstruction methods incorporating Deep Learning have gained a lot of traction in the recent past. However, most of these methods have been evaluated in human MRI. In this work, we show the feasibility of deep-learning artifact removal for the tiny golden angle radial trajectory in rodent cardiac MRI and validate two approaches against a Compressed Sensing reconstruction and the gated reference standard. The deep-learning based methods achieve acceptable visual image quality and exhibit only slight, but for one method significant, differences in the functional analysis.
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