The introduction of machine learning for medical image reconstruction has opened up new opportunities for reconstruction speed and subsampling; however, acquiring ground truth data is expensive or impossible in the case of dynamic imaging. Here we investigate a technique for optimizing a CNN on continuous radial data by treating the NUFFT-CNN function as an autoencoding deep image prior. Using this method, we are able to reconstruct images that increment over time frames as short as a single spoke. The technique opens up new possibilities for dynamic image reconstruction.
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