activity
20162021
most citedSelf-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data

61 citations · 188 across the 7 of their papers we have counts for

collaborators

7 papers

physics.med-ph2021

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…

eess.IV2021★ 9 cited

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…

physics.med-ph2020★ 21 cited

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…

eess.IV2020★ 51 cited

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…

eess.IV2019★ 61 cited

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…

eess.IV2019★ 41 cited

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…

cs.LG2016★ 5 cited

Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging

Morteza Mardani, Georgios B. Giannakis, Kamil Ugurbil

Magnetic resonance imaging (MRI) nowadays serves as an important modality for diagnostic and therapeutic guidance in clinics. However, the {\it slow acquisition} process, the dynam…