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The integration of compressed sensing and parallel imaging (CS-PI) provides arobust mechanism for accelerating MRI acquisitions. However, most suchstrategies require the explicit formation of either coil sensitivity profilesor a cross-coil correlation operator, and as a result reconstructioncorresponds to solving a challenging bilinear optimization problem. In thiswork, we present an unsupervised deep learning framework for calibration-freeparallel MRI, coined universal generative modeling for parallel imaging(UGM-PI). More precisely, we make use of the merits of both wavelet transformand the adaptive iteration strategy in a unified framework. We train a powerfulnoise conditional score network by forming wavelet tensor as the network inputat the training phase. Experimental results on both physical phantom and invivo datasets implied that the proposed method is comparable and even superiorto state-of-the-art CS-PI reconstruction approaches.

Universal Generative Modeling for Calibration-free Parallel Mr Imaging