An optimization-based meta-learning model for mri reconstruction with diverse dataset
This work aims at developing a generalizable Magnetic Resonance Imaging (MRI)
reconstruction method in the meta-learning framework. Specifically, we develop a deep …
reconstruction method in the meta-learning framework. Specifically, we develop a deep …
MEDL‐Net: A model‐based neural network for MRI reconstruction with enhanced deep learned regularizers
X Qiao, Y Huang, W Li - Magnetic Resonance in Medicine, 2023 - Wiley Online Library
Purpose To improve the MRI reconstruction performance of model‐based networks and to
alleviate their large demand for GPU memory. Methods A model‐based neural network with …
alleviate their large demand for GPU memory. Methods A model‐based neural network with …
Generalizing supervised deep learning mri reconstruction to multiple and unseen contrasts using meta-learning hypernetworks
S Ramanarayanan, A Palla, K Ram… - Applied Soft …, 2023 - Elsevier
Meta-learning has recently been an emerging data-efficient learning technique for various
medical imaging operations and has helped advance contemporary deep learning models …
medical imaging operations and has helped advance contemporary deep learning models …
Self-supervised learning for mri reconstruction with a parallel network training framework
Image reconstruction from undersampled k-space data plays an important role in
accelerating the acquisition of MR data, and a lot of deep learning-based methods have …
accelerating the acquisition of MR data, and a lot of deep learning-based methods have …
[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 …
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 …
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 …
Deep MRI reconstruction: unrolled optimization algorithms meet neural networks
Image reconstruction from undersampled k-space data has been playing an important role
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …
A Collaborative Model-driven Network for MRI Reconstruction
X Qiao, W Li, G Wang, Y Huang - arXiv preprint arXiv:2402.03383, 2024 - arxiv.org
Magnetic resonance imaging (MRI) is a vital medical imaging modality, but its development
has been limited by prolonged scanning time. Deep learning (DL)-based methods, which …
has been limited by prolonged scanning time. Deep learning (DL)-based methods, which …