Highly Accelerated Multi-channel 3D Knee Imaging Using Denoising Diffusion Probabilistic Model (DDPM) and GRAPPA

Deep learning methods have achieved superior reconstruction results in MRI reconstruction. Recently, denoising diffusion probabilistic models (DDPM) have demonstrated great potential in image-processing tasks. In this study, we combine denoising diffusion probabilistic models (DDPM) and GRAPPA for highly accelerated 3D imaging. The method sequentially performs DDPM and GRAPPA with specially designed sampling masks such that the benefits of the diffusion model and the availability of multi-channel data can be utilized jointly. Our results demonstrate that the proposed method can achieve an acceleration factor of up to 16 which is the product of the factors achieved by DDPM and GRAPPA alone.