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

Matthew Bendel, Rizwan Ahmad, Philip Schniter

Keywords: Generative adversarial network, inverse problems, posterior sampling, cGAN, GAN

TL;DR: A novel technique for training an agnostic cGAN which is robust to subsampling mask and acceleration rate for MRI 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.

Submission Number: 16