  The integration of compressed sensing and parallel imaging (CS-PI) provides a
robust mechanism for accelerating MRI acquisitions. However, most such
strategies require the explicit formation of either coil sensitivity profiles
or a cross-coil correlation operator, and as a result reconstruction
corresponds to solving a challenging bilinear optimization problem. In this
work, we present an unsupervised deep learning framework for calibration-free
parallel MRI, coined universal generative modeling for parallel imaging
(UGM-PI). More precisely, we make use of the merits of both wavelet transform
and the adaptive iteration strategy in a unified framework. We train a powerful
noise conditional score network by forming wavelet tensor as the network input
at the training phase. Experimental results on both physical phantom and in
vivo datasets implied that the proposed method is comparable and even superior
to state-of-the-art CS-PI reconstruction approaches.
