Compressed sensing for body MRI
The introduction of compressed sensing for increasing imaging speed in magnetic
resonance imaging (MRI) has raised significant interest among researchers and clinicians …
resonance imaging (MRI) has raised significant interest among researchers and clinicians …
Compressed sensing MRI: a review from signal processing perspective
JC Ye - BMC Biomedical Engineering, 2019 - Springer
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Inspired by recent advances in deep learning, we propose a framework for reconstructing
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss
TM Quan, T Nguyen-Duc… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical
foundations upon which the time-consuming MRI acquisition process can be accelerated …
foundations upon which the time-consuming MRI acquisition process can be accelerated …
KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
Purpose To demonstrate accurate MR image reconstruction from undersampled k‐space
data using cross‐domain convolutional neural networks (CNNs) Methods Cross‐domain …
data using cross‐domain convolutional neural networks (CNNs) Methods Cross‐domain …
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
B Yaman, SAH Hosseini, S Moeller… - Magnetic resonance …, 2020 - Wiley Online Library
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
A deep cascade of convolutional neural networks for MR image reconstruction
Abstract The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired
by recent advances in deep learning, we propose a framework for reconstructing MR images …
by recent advances in deep learning, we propose a framework for reconstructing MR images …
On the applications of robust PCA in image and video processing
Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …
matrices offers a powerful framework for a large variety of applications such as image …
Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components
Purpose To apply the low‐rank plus sparse (L+ S) matrix decomposition model to
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …
MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that
can accurately reconstruct images from undersampled k-space data with a much lower …
can accurately reconstruct images from undersampled k-space data with a much lower …