[PDF][PDF] State-of-the-art machine learning MRI reconstruction in 2020: Results of the second fastMRI challenge
MJ Muckley, B Riemenschneider… - arXiv preprint arXiv …, 2020 - hal.science
Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
MJ Muckley, B Riemenschneider… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge
PM Johnson, G Jeong, K Hammernik… - Machine Learning for …, 2021 - Springer
The 2019 fastMRI challenge was an open challenge designed to advance research in the
field of machine learning for MR image reconstruction. The goal for the participants was to …
field of machine learning for MR image reconstruction. The goal for the participants was to …
DuDoRNet: learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior
MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
Image quality affects deep learning reconstruction of MRI
The magnetic resonance imaging (MRI) process is susceptible to a wide range of artifacts
caused by various sources. In some cases, artifacts might be confused with pathology. In …
caused by various sources. In some cases, artifacts might be confused with pathology. In …
[HTML][HTML] A review and experimental evaluation of deep learning methods for MRI reconstruction
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …
based machine-learning techniques have received significant interest for accelerating …
Benchmarking deep nets MRI reconstruction models on the fastmri publicly available dataset
The MRI reconstruction field lacked a proper data set that allowed for reproducible results on
real raw data (ie complex-valued), especially when it comes to deep learning (DL) methods …
real raw data (ie complex-valued), especially when it comes to deep learning (DL) methods …
Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data
B Yaman, SAH Hosseini, S Moeller… - 2020 IEEE 17th …, 2020 - ieeexplore.ieee.org
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 …
common strategy among DL methods is the physics-based approach, where a regularized …
Stable deep MRI reconstruction using generative priors
Data-driven approaches recently achieved remarkable success in magnetic resonance
imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to …
imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to …
IR-FRestormer: Iterative refinement with fourier-based restormer for accelerated MRI reconstruction
Accelerated magnetic resonance imaging (MRI) aims to reconstruct high-quality MR images
from a set of under-sampled measurements. State-of-the-art methods for this task use deep …
from a set of under-sampled measurements. State-of-the-art methods for this task use deep …