716 citations · 2.8k across the 74 of their papers we have counts for
6 papers · 2 filters
CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry
Sungjun Lim, Hyoungjun Park, Sang-Eun Lee +2
Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depend…
Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS)…
Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
Yoseob Han, Junyoung Kim, Jong Chul Ye
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm…
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
Dongwook Lee, Won-Jin Moon, Jong Chul Ye
Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from…
Deep Learning-based Universal Beamformer for Ultrasound Imaging
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usua…
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray c…