455 citations · 1.5k across the 28 of their papers we have counts for
4 papers · 1 filter
AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI
Cagan Alkan, Morteza Mardani, Congyu Liao +3
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
Ke Lei, Morteza Mardani, John M. Pauly +1
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this paper leverages unpaired advers…
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Joseph Y. Cheng, Feiyu Chen, Christopher Sandino +3
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolut…