Autocalibration signal-guided multi-frequency diffusion model for dynamic MRI reconstruction.

OBJECTIVE: The autocalibration-signal (ACS) region plays a pivotal role in dynamic magnetic resonance imaging (MRI) reconstruction, yet its full potential remains under-exploited in current generative frameworks, which rely mainly on data-driven priors while overlooking ACS-embedded physical information. This work aims to propose a novel multi-frequency aware diffusion model guided by structural priors derived from the ACS region to enhance the quality of dynamic MRI reconstruction. APPROACH: Specifically, the intrinsic phase consistency and spatial smoothness within the ACS region are leveraged to construct a robust prior that stabilizes the diffusion trajectory. This prior further guides the restoration of high-frequency components, thereby improving temporal coherence and structural fidelity.Furthermore, the non-ACS region is divided into two complementary frequency bands, each jointly modeled with the ACS region to establish direction-aware diffusion pathways across the frequency domain. To further refine reconstruction performance, we incorporate low-rank regularization across time frames and enforce data-consistency constraints, which effectively suppress motion artifacts while preserving fine anatomical details. MAIN RESULTS: Quantitative evaluations demonstrate that the proposed method achieves competitive reconstruction quality across various sampling patterns and acceleration factors. Under Radial sampling at an acceleration factor of R=8, it attains PSNR of 32.53±0.34 dB, NMSE of 0.011±0.001 and SSIM of 0.8583±0.057, outperforming all competing methods. Notably, the proposed method achieves competitive performance comparable to supervised models without requiring any fully-sampled data or external training labels. SIGNIFICANCE: The proposed method enables high-quality dynamic MRI reconstruction by exploiting intrinsic ACS information, offering a practical solution without fully-sampled training data.