Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI
X Liu, Y Pang, R Jin, Y Liu… - Magnetic Resonance in …, 2022 - Wiley Online Library
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 …
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 …
HIWDNet: a hybrid image-wavelet domain network for fast magnetic resonance image reconstruction
C Tong, Y Pang, Y Wang - Computers in Biology and Medicine, 2022 - Elsevier
Abstract The application of Magnetic Resonance Imaging (MRI) is limited due to the long
acquisition time of k-space signals. Recently, many deep learning-based MR image …
acquisition time of k-space signals. Recently, many deep learning-based MR image …
A k‐space‐to‐image reconstruction network for MRI using recurrent neural network
Purpose Reconstructing the images from undersampled k‐space data are an ill‐posed
inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic …
inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic …
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 …
DIIK-Net: A full-resolution cross-domain deep interaction convolutional neural network for MR image reconstruction
Y Liu, Y Pang, X Liu, Y Liu, J Nie - Neurocomputing, 2023 - Elsevier
Acquiring incomplete k-space matrices is an effective way to accelerate Magnetic
Resonance Imaging (MRI). It is an important and challenging task to accurately reconstruct …
Resonance Imaging (MRI). It is an important and challenging task to accurately reconstruct …
Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Image reconstruction from undersampled k-space data has been playing an important role
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …
Multiple slice k-space deep learning for magnetic resonance imaging reconstruction
Magnetic resonance imaging (MRI) has been one of the most powerful and valuable
imaging methods for medical diagnosis and staging of disease. Due to the long scan time of …
imaging methods for medical diagnosis and staging of disease. Due to the long scan time of …
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 …
Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction
J Huang, S Wang, G Zhou, W Hu, G Yu - Magnetic resonance imaging, 2022 - Elsevier
Recently, deep learning approaches with various network architectures have drawn
significant attention from the magnetic resonance imaging (MRI) community because of their …
significant attention from the magnetic resonance imaging (MRI) community because of their …