Magnetic resonance imaging (MRI) reconstruction from undersampled data is central to scan acceleration. This study compares the diffusion models MC-DDPM and HFS-SDE for MRI reconstruction, trained and tested on the same dataset under identical experimental conditions, thereby addressing a common heterogeneity in the literature, where results are reported with different datasets, sampling masks, and input formats, hindering direct comparison. Acceleration factors R = 4, 8, 12, and 16 are evaluated using objective image-quality metrics (PSNR, SSIM, and NMSE). Results show that MC-DDPM consistently outperforms HFS-SDE across all scenarios, with larger gaps at higher accelerations. These findings indicate that standardized protocols make the relative strengths and limitations of the evaluated diffusion-based models clearer.
Ferreira et al. (Tue,) studied this question.