61 citations · 188 across the 7 of their papers we have counts for
4 papers · 1 filter
GRAPPA-GANs for Parallel MRI Reconstruction
Nader Tavaf, Amirsina Torfi, Kamil Ugurbil +1
k-space undersampling is a standard technique to accelerate MR image acquisitions. Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel Acquisition(GRAP…
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
Burhaneddin Yaman, Hongyi Gu, Seyed Amir Hossein Hosseini +5
Self-supervised learning has shown great promise due to its capability to train deep learning MRI reconstruction methods without fully-sampled data. Current self-supervised learnin…
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller +3
Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterati…
Accelerated Coronary MRI with sRAKI: A Database-Free Self-Consistent Neural Network k-space Reconstruction for Arbitrary Undersampling
Seyed Amir Hossein Hosseini, Chi Zhang, Sebastian Weingärtner +4
This study aims to accelerate coronary MRI using a novel reconstruction algorithm, called self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI). sRAKI…