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 …
Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received interest as a means of accelerating …
based machine-learning techniques have received interest as a means of accelerating …
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 …
Joint deep model-based MR image and coil sensitivity reconstruction network (joint-ICNet) for fast MRI
Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution
and high-contrast images. However, MRI requires a relatively long scan time compared to …
and high-contrast images. However, MRI requires a relatively long scan time compared to …
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 …
Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method
Quantitative tissue characteristics, which provide valuable diagnostic information, can be
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
Deep learning for brain disorders: from data processing to disease treatment
In order to reach precision medicine and improve patients' quality of life, machine learning is
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction
This study developed a domain-transform framework comprising domain-transform manifold
learning with an initial analytic transform to accelerate Cartesian magnetic resonance …
learning with an initial analytic transform to accelerate Cartesian magnetic resonance …
An unsupervised deep learning method for multi-coil cine MRI
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI)
reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the …
reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the …
Assessing pediatric mild traumatic brain injury and its recovery using resting-state magnetoencephalography source magnitude imaging and machine learning
MX Huang, A Angeles-Quinto, A Robb-Swan… - Journal of …, 2023 - liebertpub.com
The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-
MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury …
MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury …