Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net
Moritz Blumenthal,
Chiara Fantinato,
Christina Unterberg‐Buchwald
et al.
Abstract:PurposeTo develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training.MethodsNLINV‐Net is a model‐based neural network architecture that directly estimates images and coil sensitivities from (radial) k‐space data via nonlinear inversion (NLINV). Combined with a training strategy using self‐supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We valid… Show more
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