The primary focus of this work is to introduce a novel deep learning framework, which synergistically combines the benefits of model-based image recovery with the power of deep learning. This work enables the easy exploitation of prior information available from calibration scans, in addition to significantly reducing the number of network parameters, amount of training data required, and computational complexity. More importantly, the insensitivity of the learned model to the acquisition parameters also facilitates its easy reuse with a range of acquisition settings.
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