Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution
Magnetic resonance imaging (MRI) can present multi-contrast images of the same
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …
Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction
Cardiac cine magnetic resonance imaging (MRI) reconstruction is challenging due to spatial
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction
X Zhao, T Yang, B Li, X Zhang - Computers in Biology and Medicine, 2023 - Elsevier
Magnetic resonance imaging (MRI) is one of the most important modalities for clinical
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …
[HTML][HTML] Narrative review of generative adversarial networks in medical and molecular imaging
Recent years have witnessed a rapidly expanding use of artificial intelligence and machine
learning in medical imaging. Generative adversarial networks (GANs) are techniques to …
learning in medical imaging. Generative adversarial networks (GANs) are techniques to …
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate
motion artefacts and increase patient throughput. K-space undersampling is an obvious …
motion artefacts and increase patient throughput. K-space undersampling is an obvious …
TR-Gan: multi-session future MRI prediction with temporal recurrent generative adversarial Network
Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose
Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the …
Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the …
Tc-kanrecon: High-quality and accelerated mri reconstruction via adaptive kan mechanisms and intelligent feature scaling
Magnetic Resonance Imaging (MRI) has become essential in clinical diagnosis due to its
high resolution and multiple contrast mechanisms. However, the relatively long acquisition …
high resolution and multiple contrast mechanisms. However, the relatively long acquisition …
IKWI-net: A cross-domain convolutional neural network for undersampled magnetic resonance image reconstruction
Z Wang, H Jiang, H Du, J Xu, B Qiu - Magnetic resonance imaging, 2020 - Elsevier
Magnetic resonance imaging (MRI) is widely used to get the information of anatomical
structure and physiological function with the advantages of high resolution and non-invasive …
structure and physiological function with the advantages of high resolution and non-invasive …
GRAPPA-GANs for parallel MRI reconstruction
k-space undersampling is a standard technique to accelerate MR image acquisitions.
Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel …
Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel …