Retrospective k-Space Synthesis for Cardiac MRI Deep-learning Applications from Magnitude-only Images Using Score-based Diffusion Models

Motivation: Developing deep learning (DL)-based image reconstruction techniques requires raw k-space datasets. The use of magnitude-only MRI images (DICOMs) to obtain k-space can be prohibitive for training robust models. Goal(s): To synthesize phase-maps of DCE cardiac MRI from magnitude-only images by using the recently emerging diffusion models. Approach: A conditional score-based diffusion model (SBDM) is trained to synthesize phase-maps from the magnitude-only images. The value of the synthesized phase-maps is assessed with a DL-based image reconstruction model. Results: SBDM-derived phase-maps outperformed random and GAN-based phase-map generation methods in terms of reconstruction performance. Qualitative assessment suggests that SBDMs can generate realistic-looking phase-maps. Impact: We proposed to leverage the emerging generative diffusion models for retrospective phase-map synthesis of DCE cardiac MRI from the magnitude-only images which has the potential to create large k-space datasets using the magnitude-only multi-center registries to improve deep learning-based reconstruction.