Synthesizing raw multicoil MRI data with a deep generative framework.

Authors

Nikhil Deveshwar, Abhejit Rajagopal, Michael Lustig, Peder E Z Larson

Published in

Journal of medical imaging (Bellingham, Wash.). Volume 13. Issue Suppl 1. Pages S11211. Epub Oct 16, 2026.

Abstract

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.

PMID:
42891434
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.

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