Adaptive Mask-Guided k-Space Diffusion for Accelerated MRI Reconstruction

Masked modeling has emerged as a powerful deep learning paradigm that reconstructs missing data by predicting masked regions, achieving strong performance across multiple domains. As a critical task in medical imaging, magnetic resonance imaging (MRI) reconstruction aims to recover high-quality images from under-sampled k-space data. However, existing methods usually optimize the full image domain or k-space, while overlooking the importance and structural roles of different frequency components.

An adaptive mask-guided diffusion model (AMDM) was developed, which leverages adaptively estimated frequency distributions from k-space to construct a hybrid mask mechanism for handling diverse k-space inputs. It enables the effective separation of high-frequency and low-frequency components, producing multiple prior representations. In addition, the frequency distribution is used to generate adaptive masks, which subsequently guide the closed-loop diffusion process.

Experimental results demonstrated improved reconstruction performance through frequency-aware modeling. AMDM achieved a PSNR of 36.89 dB on an independent public brain dataset without retraining, even at an under-sampling factor of $R=12$ .

The proposed method shows that explicitly modeling k-space frequency heterogeneity provides an effective prior for MRI reconstruction, offering a promising direction for robust reconstruction under aggressive under-sampling.