Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionDeep learning (DL) assisted image reconstructions are becoming state-of-the art, producing better image quality and/or enabling higher acceleration rates than achievable with conventional methods. DL networks are used to mitigate noise amplification while retaining important signal characteristics. However, typical loss functions produce object-dependent noise alterations and non-uniform point-spread functions. Here we present a method for training networks that prioritizes maximizing the point spread function to ensure maximal detail retention.
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