Physics-Informed Cold Diffusion Framework with Time-Conditioned U-Net for Multi-Coil Brain MRI Reconstruction - Grčić, Nives; Habijan, Marija; Matić, Krešimir; Galić, Irena (Informatica, 2026)
Magnetic Resonance Imaging (MRI) reconstruction from undersampled multi-coil k-space data remains a central challenge for accelerating clinical scans. This paper proposes a cold diffusion based reconstruction framework that integrates a deterministic k-space forward operator with a time-conditioned U-Net denoiser. Unlike conventional diffusion models that rely on stochastic Gaussian noise, the proposed approach explicitly models the physical undersampling process, yielding interpretable and data-consistent reconstructions. The model was trained and evaluated on the fastMRI multi-coil brain dataset using 2D slices center-cropped to 320×320 and normalized in complex form (real + imaginary channels). Ablation studies compare the proposed method against a supervised U-Net baseline and a Gaussian DDPM trained under identical conditions. Quantitatively, the cold diffusion model with 500 diffusion steps achieved 37.8 dB PSNR, 0.42 SSIM, and 0.46 HFEN, outperforming both the DDPM (32.8 dB, 0.33 SSIM, 0.60 HFEN) and supervised U-Net (28.9 dB, 0.29 SSIM, 0.71 HFEN) while reducing inference time by 70% relative to conventional diffusion sampling. Qualitative results indicate improved anatomical sharpness, stable convergence and consistent reconstruction across slices, suggesting that the physics-informed diffusion framework is a reliable and interpretable approach for accelerated MRI reconstruction.