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

SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging