33 citations · 34 across the 5 of their papers we have counts for
4 papers · 1 filter
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong +1
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand b…
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala +1
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground…
Reconstruction of Undersampled 3D Non-Cartesian Image-Based Navigators for Coronary MRA Using an Unrolled Deep Learning Model
Mario O. Malavé, Corey A. Baron, Srivathsan P. Koundinyan +4
Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model for non-rigid motion correction in coronary…
Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging
Jonathan I. Tamir, Frank Ong, Suma Anand +3
Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often t…