2024
DOI: 10.1002/mrm.30234
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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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