MRI reconstruction using DDPM with sparsely sampled k-space as guidance

Recovering MR images from partially acquired k-space data can reduce scan time, leading to significant applications. Deep learning approaches have been proposed to train neural networks using under-sampled and full-sampled image pairs in a supervised manner. However, these supervised models usually have poor generalizability when deployed to acquisitions different from the training datasets. Here, we propose an unsupervised Denoising Diffusion Probabilistic Model (DDPM) capable of not only generating random high-fidelity MR images but also reconstructing images corresponding to k-space data from arbitrary acquisition patterns.