Diffusion Models for MRI Reconstruction: A Systematic Review of Standard, Hybrid, Latent and Cold Diffusion Approaches

Diffusion models have recently emerged as a new paradigm to solve inverse problems in magnetic resonance imaging (MRI). This paper systematically categorizes forty representative studies that use diffusion models for MRI reconstruction. The studies are systematically divided into three dimensional categories: (1) model type distribution that includes standard, score-based diffusion, hybrid and physics-informed diffusion, latent diffusion and cold diffusion; (2) application domain distribution that includes accelerated, motion-corrected, dynamic, quantitative and cross-modal MRI; and (3) domain of reconstruction distribution that includes image-space, k-space, latent-space and hybrid approaches. In this context, diffusion-driven methods represent a significant methodological shift, evolving from purely data-driven denoising toward unified, physics-aligned reconstruction frameworks. Hybrid and cold diffusion approaches further integrate probabilistic generative priors with explicit MRI acquisition physics, which enables improved fidelity, artifact suppression and enhanced generalization across sampling regimes and anatomical domains. This study reviews current advances in the field of MRI reconstruction methods and gives future research directions, emphasizing the need for interpretable, data-efficient and multi-domain generative models that are capable of robustly addressing the diverse challenges of MRI reconstruction.