3 papers
eess.IV2026
UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
Mahdi Saberi, Toygan Kiliç, Mehmet Akçakaya
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…
physics.med-ph2019
Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions
Emre Kopanoglu, Alper Güngör, Toygan Kilic +4
Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acqu…
eess.IV2017
Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI
L Kerem Senel, Toygan Kilic, Alper Gungor +5
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techni…