20 citations · 27 across the 6 of their papers we have counts for
6 papers
DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrieval
Eunju Cha, Chanseok Lee, Mooseok Jang +1
Fourier phase retrieval is a classical problem of restoring a signal only from the measured magnitude of its Fourier transform. Although Fienup-type algorithms, which use prior kno…
Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data
Hyungjin Chung, Eunju Cha, Leonard Sunwoo +1
Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-D volume acquisi…
Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution
Eunju Cha, Hyungjin Chung, Eung Yeop Kim +1
Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the k-spac…
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Eunju Cha, Gyutaek Oh, Jong Chul Ye
Recently, deep learning approaches have been extensively investigated to reconstruct images from accelerated magnetic resonance image (MRI) acquisition. Although these approaches p…
Boosting CNN beyond Label in Inverse Problems
Eunju Cha, Jaeduck Jang, Junho Lee +2
Convolutional neural networks (CNN) have been extensively used for inverse problems. However, their prediction error for unseen test data is difficult to estimate a priori since th…
k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
Eunju Cha, Eung Yeop Kim, Jong Chul Ye
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisiti…