5 citations · 7 across the 4 of their papers we have counts for
4 papers
Deep learning-based reconstruction of highly accelerated 3D MRI
Sangtae Ahn, Uri Wollner, Graeme McKinnon +10
Purpose: To accelerate brain 3D MRI scans by using a deep learning method for reconstructing images from highly-undersampled multi-coil k-space data Methods: DL-Speed, an unrolled…
Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation
Itzik Malkiel, Sangtae Ahn, Valentina Taviani +3
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling muc…
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
Michael Rotman, Rafi Brada, Israel Beniaminy +3
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many method…
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
Itzik Malkiel, Sangtae Ahn, Valentina Taviani +3
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much fas…