  Score matching with Langevin dynamics (SMLD) method has been successfully
applied to accelerated MRI. However, the hyperparameters in the sampling
process require subtle tuning, otherwise the results can be severely corrupted
by hallucination artifacts, especially with out-of-distribution test data. To
address the limitations, we proposed a novel workflow where naive SMLD samples
serve as additional priors to guide model-driven network training. First, we
adopted a pretrained score network to generate samples as preliminary guidance
images (PGI), obviating the need for network retraining, parameter tuning and
in-distribution test data. Although PGIs are corrupted by hallucination
artifacts, we believe they can provide extra information through effective
denoising steps to facilitate reconstruction. Therefore, we designed a
denoising module (DM) in the second step to coarsely eliminate artifacts and
noises from PGIs. The features are extracted from a score-based information
extractor (SIE) and a cross-domain information extractor (CIE), which directly
map to the noise patterns. Third, we designed a model-driven network guided by
denoised PGIs (DGIs) to further recover fine details. DGIs are densely
connected with intermediate reconstructions in each cascade to enrich the
information and are periodically updated to provide more accurate guidance. Our
experiments on different datasets reveal that despite the low average quality
of PGIs, the proposed workflow can effectively extract valuable information to
guide the network training, even with severely reduced training data and
sampling steps. Our method outperforms other cutting-edge techniques by
effectively mitigating hallucination artifacts, yielding robust and
high-quality reconstruction results.
