Posterior Sampling for Accelerated Multicoil MRI Reconstruction using a Conditional Normalizing Flow

For accelerated MR image reconstruction, machine learning (ML)-based methods outperform traditional sparsity-based methods by exploiting large datasets to learn effective priors. However, most ML methods output only a single image reconstruction when in fact there may be many plausible reconstructions given the measurement and prior. To extract this diagnostically relevant information, we propose to explore the space of plausible images, i.e., to sample the posterior, using ML. Among ML methods, conditional normalizing flows (CNFs) stand out for rapid sample generation and simple likelihood-based training. In this work, we present the first CNF for posterior sample generation in accelerated multicoil MRI.