9 citations · 9 across the 2 of their papers we have counts for
5 papers
Designing a Self-Decoupled 16 Channel Transmitter for Human Brain Magnetic Resonance Imaging at 447MHz
Nader Tavaf, Jerahmie Radder, Russell L. Lagore +5
Transmitter arrays play a critical role in ultra high field Magnetic Resonance Imaging (MRI), especially given the advantages made possible via parallel transmission (pTx) techniqu…
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…
A Self-Decoupled 32 Channel Receive Array for Human Brain Magnetic Resonance Imaging at 10.5T
Nader Tavaf, Russell L. Lagore, Steve Jungst +8
Purpose: Receive array layout, noise mitigation and B0 field strength are crucial contributors to signal-to-noise ratio (SNR) and parallel imaging performance. Here, we investigate…
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…