MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Magnetic Resonance Imaging (MRI) reconstruction is essential in medical
diagnostics. As the latest generative models, diffusion models (DMs) have
struggled to produce high-fidelity images due to their stochastic nature in
image domains. Latent diffusion models (LDMs) yield both compact and detailed
prior knowledge in latent domains, which could effectively guide the model
towards more effective learning of the original data distribution. Inspired by
this, we propose Multi-domain Diffusion Prior Guidance (MDPG) provided by
pre-trained LDMs to enhance data consistency in MRI reconstruction tasks.
Specifically, we first construct a Visual-Mamba-based backbone, which enables
efficient encoding and reconstruction of under-sampled images. Then pre-trained
LDMs are integrated to provide conditional priors in both latent and image
domains. A novel Latent Guided Attention (LGA) is proposed for efficient fusion
in multi-level latent domains. Simultaneously, to effectively utilize a prior
in both the k-space and image domain, under-sampled images are fused with
generated full-sampled images by the Dual-domain Fusion Branch (DFB) for
self-adaption guidance. Lastly, to further enhance the data consistency, we
propose a k-space regularization strategy based on the non-auto-calibration
signal (NACS) set. Extensive experiments on two public MRI datasets fully
demonstrate the effectiveness of the proposed methodology. The code is
available at https://github.com/Zolento/MDPG.
