AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
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 …
A deep Siamese convolution neural network for multi-class classification of Alzheimer disease
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which
results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a …
results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a …
Emerging trends in fast MRI using deep-learning reconstruction on undersampled k-space data: a systematic review
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
Dual-domain cascade of U-nets for multi-channel magnetic resonance image reconstruction
The U-net is a deep-learning network model that has been used to solve a number of
inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net …
inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net …
Deep learning based MRI reconstruction with transformer
Z Wu, W Liao, C Yan, M Zhao, G Liu, N Ma… - Computer Methods and …, 2023 - Elsevier
Magnetic resonance imaging (MRI) has become one of the most powerful imaging
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
A review of deep learning methods for compressed sensing image reconstruction and its medical applications
Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …
paper, we review recent works using deep learning method to solve CS problem for images …
Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network
Multidimensional nuclear magnetic resonance (NMR) spectroscopy is used to examine the
chemical structures of the studied systems. Unfortunately, the application of NMR spectra is …
chemical structures of the studied systems. Unfortunately, the application of NMR spectra is …
pFISTA-SENSE-ResNet for parallel MRI reconstruction
Magnetic resonance imaging has been widely applied in clinical diagnosis. However, it is
limited by its long data acquisition time. Although the imaging can be accelerated by sparse …
limited by its long data acquisition time. Although the imaging can be accelerated by sparse …
Multi-coil mri reconstruction challenge—assessing brain mri reconstruction models and their generalizability to varying coil configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods
have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific …
have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific …