  Lately, deep learning has been extensively investigated for accelerating
dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved.
However, without fully sampled reference data for training, current approaches
may have limited abilities in recovering fine details or structures. To address
this challenge, this paper proposes a self-supervised collaborative learning
framework (SelfCoLearn) for accurate dynamic MR image reconstruction from
undersampled k-space data. The proposed framework is equipped with three
important components, namely, dual-network collaborative learning,
reunderampling data augmentation and a specially designed co-training loss. The
framework is flexible to be integrated with both data-driven networks and
model-based iterative un-rolled networks. Our method has been evaluated on
in-vivo dataset and compared it to four state-of-the-art methods. Results show
that our method possesses strong capabilities in capturing essential and
inherent representations for direct reconstructions from the undersampled
k-space data and thus enables high-quality and fast dynamic MR imaging.
