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 …
Accelerated MRI with un-trained neural networks
MZ Darestani, R Heckel - IEEE Transactions on Computational …, 2021 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction
problems. Typically, CNNs are trained on large amounts of training images. Recently …
problems. Typically, CNNs are trained on large amounts of training images. Recently …
Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of
undersampled and noisy measurements. Deep learning approaches have been proven to …
undersampled and noisy measurements. Deep learning approaches have been proven to …
Near-exact recovery for tomographic inverse problems via deep learning
This work is concerned with the following fundamental question in scientific machine
learning: Can deep-learning-based methods solve noise-free inverse problems to near …
learning: Can deep-learning-based methods solve noise-free inverse problems to near …
NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction
Deep Learning has become a very promising avenue for magnetic resonance image (MRI)
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI
Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
[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 …
IDPCNN: Iterative denoising and projecting CNN for MRI reconstruction
Compressed sensing magnetic resonance imaging (CS-MRI) makes it possible to shorten
data acquisition time substantially. The traditional iteration-based CS-MRI method is flexible …
data acquisition time substantially. The traditional iteration-based CS-MRI method is flexible …
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 …
DIR3D: Cascaded Dual-Domain Inter-Scale Mutual Reinforcement 3D Network for highly accelerated 3D MR image reconstruction
Deep Learning has been successfully applied to reconstruct Magnetic Resonance (MR)
images from undersampled k-space data to achieve the acceleration of MRI. However, most …
images from undersampled k-space data to achieve the acceleration of MRI. However, most …