Fast Controllable Diffusion Models for Undersampled MRI Reconstruction

arXiv:2311.12078v3 Announce Type: replace-cross
Abstract: Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters. Recently, unsupervised controllable generative diffusion models have been applied to undersampled MRI reconstruction, without paired data or model retraining for different MRI acquisitions. However, diffusion models are generally slow in sampling and state-of-the-art acceleration techniques can lead to sub-optimal results when directly applied to the controllable …

abstract acquisition arxiv cs.lg data deep learning diffusion diffusion models diverse eess.iv generative imaging mri parameters replace type unsupervised