Super-resolution reconstruction of MR image with a novel residual learning network algorithm
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The
image super-resolution (SR) technique offers an alternative approach to improve the spatial …
image super-resolution (SR) technique offers an alternative approach to improve the spatial …
MR image super-resolution via wide residual networks with fixed skip connection
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
Progressive sub-band residual-learning network for MR image super resolution
High-resolution (HR) magnetic resonance images (MRI) provide more detailed information
for clinical application. However, HR MRI is less available because of the longer scan time …
for clinical application. However, HR MRI is less available because of the longer scan time …
MRI super-resolution via realistic downsampling with adversarial learning
B Huang, H Xiao, W Liu, Y Zhang, H Wu… - Physics in Medicine …, 2021 - iopscience.iop.org
Many deep learning (DL) frameworks have demonstrated state-of-the-art performance in the
super-resolution (SR) task of magnetic resonance imaging, but most performances have …
super-resolution (SR) task of magnetic resonance imaging, but most performances have …
Residual dense network for medical magnetic resonance images super-resolution
Background and objective High-resolution magnetic resonance images (MRI) help experts
to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI …
to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI …
Brain MRI super-resolution using coupled-projection residual network
Abstract Magnetic Resonance Imaging (MRI) has been widely used in clinical application
and pathology research to help doctors provide better diagnoses. However, accurate …
and pathology research to help doctors provide better diagnoses. However, accurate …
Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …
information important for clinical application and quantitative image analysis. However, HR …
Autoencoder-inspired convolutional network-based super-resolution method in MRI
Objective: To introduce an MRI in-plane resolution enhancement method that estimates
High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous …
High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous …
A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond
The progress of convolution neural network (CNN) based super-resolution has shown its
potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) …
potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) …
MRI super-resolution with ensemble learning and complementary priors
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However,
due to the limitations in hardware, scan time, and throughput, it is often clinically challenging …
due to the limitations in hardware, scan time, and throughput, it is often clinically challenging …