37 citations · 48 across the 3 of their papers we have counts for
7 papers
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations
Chi Zhang, Jinghan Jia, Burhaneddin Yaman +4
Although deep learning (DL) has received much attention in accelerated MRI, recent studies suggest small perturbations may lead to instabilities in DL-based reconstructions, leadin…
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…
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +1
Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularize…
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +2
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics…
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…