716 citations · 2.8k across the 74 of their papers we have counts for
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One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
Yoseob Han, Jong Chul Ye
Computed tomography for region-of-interest (ROI) reconstruction has advantages of reducing X-ray radiation dose and using a small detector. However, standard analytic reconstructio…
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…
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
Eunhee Kang, Hyun Jung Koo, Dong Hyun Yang +2
In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image…
k-Space Deep Learning for Reference-free EPI Ghost Correction
Juyoung Lee, Yoseob Han, Jae-Kyun Ryu +2
Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erro…
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Dongwook Lee, Jaejun Yoo, Sungho Tak +1
Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruc…
Quantitative Susceptibility Map Reconstruction Using Annihilating Filter-based Low-Rank Hankel Matrix Approach
Hyun-Seo Ahn, Sung-Hong Park, Jong Chul Ye
Quantitative susceptibility mapping (QSM) inevitably suffers from streaking artifacts caused by zeros on the conical surface of the dipole kernel in k-space. This work proposes a n…