A Regularized Conditional GAN for Posterior Sampling in MR Image Reconstruction

For magnetic resonance (MR) image reconstruction, Fourier-domain measurements are collected at rates far below Nyquist to reduce clinical exam time. Because many plausible reconstructions exist that are consistent with a given measurement, we use machine learning to sample from the posterior distribution rather than generate a single image reconstruction. Many such works leverage score-based generative models (SGMs), which seek to iteratively denoise a random input but require many minutes to generate each sample. We propose a conditional generative adversarial network (GAN) that generates hundreds of posterior samples per minute and outperforms the current state-of-the-art SGM for multi-coil MR posterior sampling.