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, particularly with out-of-distribution test data. In
this study, we propose a novel workflow in which SMLD results are regarded as
additional priors to guide model-driven network training. First, we adopted a
pretrained score network to obtain samples as preliminary guidance images (PGI)
without the need for network retraining, parameter tuning and in-distribution
test data. Although PGIs are corrupted by hallucination artifacts, we believe
that they can provide extra information through effective denoising steps to
facilitate reconstruction. Therefore, we designed a denoising module (DM) in
the second step to improve the quality of PGIs. The features are extracted from
the components of Langevin dynamics and the same score network with
fine-tuning; hence, we can directly learn the artifact patterns. Third, we
designed a model-driven network whose training is guided by denoised PGIs
(DGIs). DGIs are densely connected with intermediate reconstructions in each
cascade to enrich the features and are periodically updated to provide more
accurate guidance. Our experiments on different sequences revealed 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.

使用 Langevin 动力学的得分匹配（SMLD）方法已成功应用于加速 MRI。本研究提出一种新的工作流，将 SMLD 结果作为额外的先验来指导模型驱动的网络训练，通过引入预训练的网络和去噪模块，有效改善 PGI 的质量，并设计一个模型驱动的网络，在低质量的 PGI 中提取有价值的信息来指导网络训练，以减轻幻觉伪影，获得稳定和高质量的重建结果。