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20172023
most citedDeep Generative Adversarial Networks for Compressed Sensing Automates MRI

132 citations · 409 across the 19 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.IV2019★ 4 cited

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Jeffrey Ma, Ukash Nakarmi, Cedric Yue Sik Kin +6

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic qualit…

eess.IV2019

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…

cs.LG2019★ 5 cited

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-…

eess.IV2019★ 8 cited

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…

cs.CV2019

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…