  With the successful application of deep learning to magnetic resonance (MR)
imaging, parallel imaging techniques based on neural networks have attracted
wide attention. However, in the absence of high-quality, fully sampled datasets
for training, the performance of these methods is limited. And the
interpretability of models is not strong enough. To tackle this issue, this
paper proposes a Physics-bAsed unsupeRvised Contrastive rEpresentation Learning
(PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has a
parallel framework to contrastively learn two branches of model-based unrolling
networks from augmented undersampled multi-coil k-space data. A sophisticated
co-training loss with three essential components has been designed to guide the
two networks in capturing the inherent features and representations for MR
images. And the final MR image is reconstructed with the trained contrastive
networks. PARCEL was evaluated on two vivo datasets and compared to five
state-of-the-art methods. The results show that PARCEL is able to learn
essential representations for accurate MR reconstruction without relying on
fully sampled datasets.
