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 …
Machine learning in magnetic resonance imaging: image reconstruction
Abstract Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution
Super-resolving medical images can help physicians in providing more accurate
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
Deep spatio-temporal 3D densenet with multiscale ConvLSTM-Resnet network for citywide traffic flow forecasting
R He, Y Liu, Y Xiao, X Lu, S Zhang - Knowledge-Based Systems, 2022 - Elsevier
Reliable traffic flow forecasting is paramount in Intelligent Transportation Systems (ITS) as it
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …
VolumeNet: A lightweight parallel network for super-resolution of MR and CT volumetric data
Deep learning-based super-resolution (SR) techniques have generally achieved excellent
performance in the computer vision field. Recently, it has been proven that three …
performance in the computer vision field. Recently, it has been proven that three …
Convolutional neural networks with intermediate loss for 3D super-resolution of CT and MRI scans
Computed Tomography (CT) scanners that are commonly-used in hospitals and medical
centers nowadays produce low-resolution images, eg one voxel in the image corresponds to …
centers nowadays produce low-resolution images, eg one voxel in the image corresponds to …
Adjacent slices feature transformer network for single anisotropic 3D brain MRI image super-resolution
L Wang, H Zhu, Z He, Y Jia, J Du - Biomedical Signal Processing and …, 2022 - Elsevier
Magnetic resonance imaging (MRI) is widely used in clinical applications. However, due to
the limitations in signal-to-noise ratio, physical properties of the scanner and scanning time …
the limitations in signal-to-noise ratio, physical properties of the scanner and scanning time …
Brain MRI super-resolution using 3D dilated convolutional encoder–decoder network
The spatial resolution of magnetic resonance images (MRI) is limited by the hardware
capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep …
capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep …
MR image reconstruction using densely connected residual convolutional networks
A Aghabiglou, EM Eksioglu - Computers in Biology and Medicine, 2021 - Elsevier
MR image reconstruction techniques based on deep learning have shown their capacity for
reducing MRI acquisition time and performance improvement compared to analytical …
reducing MRI acquisition time and performance improvement compared to analytical …
Synthesizing high‐resolution magnetic resonance imaging using parallel cycle‐consistent generative adversarial networks for fast magnetic resonance imaging
Purpose The common practice in acquiring the magnetic resonance (MR) images is to
obtain two‐dimensional (2D) slices at coarse locations while keeping the high in‐plane …
obtain two‐dimensional (2D) slices at coarse locations while keeping the high in‐plane …