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Decreasing magnetic resonance (MR) image acquisition times can potentiallymake MR examinations more accessible. Prior arts including the deep learningmodels have been devoted to solving the problem of long MRI imaging time.Recently, deep generative models have exhibited great potentials in algorithmrobustness and usage flexibility. Nevertheless, none of existing schemes can belearned or employed to the k-space measurement directly. Furthermore, how dothe deep generative models work well in hybrid domain is also worth beinginvestigated. In this work, by taking advantage of the deep energy-basedmodels, we propose a k-space and image domain collaborative generative model tocomprehensively estimate the MR data from under-sampled measurement.Experimental comparisons with the state-of-the-arts demonstrated that theproposed hybrid method has less error in reconstruction accuracy and is morestable under different acceleration factors

K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction