455 citations · 1.5k across the 28 of their papers we have counts for
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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…
Degrees of Freedom Analysis of Unrolled Neural Networks
Morteza Mardani, Qingyun Sun, Vardan Papyan +3
Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-…
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…
Uncertainty Quantification in Deep MRI Reconstruction
Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala +1
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-tran…