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 …
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 …
Wide weighted attention multi-scale network for accurate MR image super-resolution
High-quality magnetic resonance (MR) images afford more detailed information for reliable
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …
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 …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
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 …
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 …
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 …
Fusing multi-scale information in convolution network for MR image super-resolution reconstruction
Background Magnetic resonance (MR) images are usually limited by low spatial resolution,
which leads to errors in post-processing procedures. Recently, learning-based super …
which leads to errors in post-processing procedures. Recently, learning-based super …
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) …