Synthesizing raw multicoil MRI data with a deep generative framework

Deep learning-based magnetic resonance imaging (MRI) reconstruction can enable faster imaging and improved image quality; however, clinical adoption is limited by the lack of diverse raw multicoil training datasets. In routine clinical workflows, multicoil $k$ -space data needed for training are often discarded, and only magnitude images are retained. In addition, phase is not deterministically recoverable from magnitude, and storing full $k$ -space is impractical.

We address these limitations by (1) using a deep generative model to produce multiple plausible synthetic phase images conditioned on a single magnitude image and (2) introducing a radiofrequency (RF) coil library to synthesize multicoil k-space from the generated complex-valued images.

The synthetic phase images resemble ground-truth phase as assessed by Fréchet inception distance, and the generated multicoil $k$ -space effectively trains unrolled MRI reconstruction networks.

These results indicate that generative phase synthesis coupled with an RF coil library provides a practical alternative to real multicoil $k$ -space for training reconstruction models.