10 citations · 22 across the 5 of their papers we have counts for
8 papers
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applica…
Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
Hyungjin Chung, Jaeyoung Huh, Geon Kim +2
Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of…
Switchable Deep Beamformer
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers…
OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Jaeyoung Huh, Shujaat Khan, Jong Chul Ye
Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches…
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfo…
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)…