  Deep learning (DL)-based methods offer a promising solution to reduce the
prolonged scanning time in magnetic resonance imaging (MRI). While model-driven
DL methods have demonstrated convincing results by incorporating prior
knowledge into deep networks, further exploration is needed to optimize the
integration of diverse priors.. Existing model-driven networks typically
utilize linearly stacked unrolled cascades to mimic iterative solution steps in
optimization algorithms. However, this approach needs to find a balance between
different prior-based regularizers during training, resulting in slower
convergence and suboptimal reconstructions. To overcome the limitations, we
propose a collaborative model-driven network to maximally exploit the
complementarity of different regularizers. We design attention modules to learn
both the relative confidence (RC) and overall confidence (OC) for the
intermediate reconstructions (IRs) generated by different prior-based
subnetworks. RC assigns more weight to the areas of expertise of the
subnetworks, enabling precise element-wise collaboration. We design correction
modules to tackle bottleneck scenarios where both subnetworks exhibit low
accuracy, and they further optimize the IRs based on OC maps. IRs across
various stages are concatenated and fed to the attention modules to build
robust and accurate confidence maps. Experimental results on multiple datasets
showed significant improvements in the final results without additional
computational costs. Moreover, the proposed model-driven network design
strategy can be conveniently applied to various model-driven methods to improve
their performance.
