This study introduces a domain-conditioned and temporally guided diffusion framework for accelerated dynamic MRI reconstruction, in which the reverse diffusion process is explicitly guided to model spatiotemporal structure in time-resolved data. The framework integrates temporal information from time-resolved dimensions, allowing for the concurrent capture of intraframe spatial features and interframe temporal dynamics in diffusion modeling. Meanwhile, it employs additional spatiotemporal and self-consistent frequency-temporal priors to guide the diffusion process, ensuring precise temporal alignment and enhancing fine image detail recovery. To facilitate a smooth diffusion process, the nonlinear conjugate gradient algorithm is utilized during the reverse diffusion steps. The proposed model was tested on two types of MRI data: Cartesian-acquired multicoil cardiac MRI and golden-angle-radial-acquired multicoil free-breathing lung MRI, across various undersampling rates. It achieved high-quality reconstructions, demonstrating improved temporal alignment and structural recovery compared with other competitive reconstruction methods, both qualitatively and quantitatively. This diffusion framework exhibited robust performance in handling both Cartesian and non-Cartesian acquisitions, effectively reconstructing dynamic datasets in cardiac and lung MRI under different imaging conditions.
Zhang et al. (Tue,) studied this question.