Mask-Agnostic Posterior Sampling MRI via Conditional GANs with Guided Reconstruction

Abstract

For accelerated magnetic resonance imaging (MRI), conditional generative adversarial networks (cGANs), when trained end-to-end with a fixed subsampling mask, have been shown to compete with contemporary diffusion-based techniques while generating samples thousands of times faster. To handle unseen sampling masks at inference, we propose ``guided reconstruction'' (GR), wherein the cGAN code vectors are projected onto the measurement subspace. Using fastMRI brain data, we demonstrate that GR allows a cGAN to successfully handle changes in sampling mask, as well as changes in acceleration rate, yielding faster and more accurate recoveries than the Langevin approach from (Jalal et al., 2021) and the DDRM diffusion approach from (Kawar et al., 2022). Our code will be made available at https://github.com/matt-bendel/rcGAN-agnostic.