Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction

Understand

We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.

• Different from previous works, MC-DDPM is defined in measurement domain (e.g.
• k-space in MRI reconstruction) and conditioned on under-sampling mask.
• We apply this method to accelerate MRI reconstruction and the experimental results show excellent performance, outperforming full supervision baseline and the state-of-the-art score-based reconstruction method.