  Purpose: Although recent deep energy-based generative models (EBMs) have
shown encouraging results in many image generation tasks, how to take advantage
of the self-adversarial cogitation in deep EBMs to boost the performance of
Magnetic Resonance Imaging (MRI) reconstruction is still desired.
  Methods: With the successful application of deep learning in a wide range of
MRI reconstruction, a line of emerging research involves formulating an
optimization-based reconstruction method in the space of a generative model.
Leveraging this, a novel regularization strategy is introduced in this article
which takes advantage of self-adversarial cogitation of the deep energy-based
model. More precisely, we advocate for alternative learning a more powerful
energy-based model with maximum likelihood estimation to obtain the deep
energy-based information, represented as image prior. Simultaneously, implicit
inference with Langevin dynamics is a unique property of re-construction. In
contrast to other generative models for reconstruction, the proposed method
utilizes deep energy-based information as the image prior in reconstruction to
improve the quality of image.
  Results: Experiment results that imply the proposed technique can obtain
remarkable performance in terms of high reconstruction accuracy that is
competitive with state-of-the-art methods, and does not suffer from mode
collapse.
  Conclusion: Algorithmically, an iterative approach was presented to
strengthen EBM training with the gradient of energy network. The robustness and
the reproducibility of the algorithm were also experimentally validated. More
importantly, the proposed reconstruction framework can be generalized for most
MRI reconstruction scenarios.
