Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural network without a database of fully sampled data sets. Methods Self‐supervised learning via data undersampling (SSDU) for physics‐guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) …