UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on t...
| Authors | Mahdi Saberi, Toygan Kiliç, Mehmet Akçakaya |
|---|---|
| Primary category | eess.IV |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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