A popular framework for reconstruction of undersampled MR acquisitions is deep neural networks (DNNs). DNNs are typically trained in a supervised manner to learn mapping between undersampled and fully sampled acquisitions. However, this approach ideally requires training a separate network for each set of contrast, acceleration rate, and sampling density, which introduces practical burden. To address this limitation, we propose a style generative model that learns MR image priors given fully sampled training dataset of specific contrast. Proposed approach is then able to efficiently recover undersampled acquisitions without any training, irrespective of the image contrast, acceleration rate or undersampling pattern.
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