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) …
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 …
Super-resolution method for MR images based on multi-resolution CNN
L Kang, G Liu, J Huang, J Li - Biomedical Signal Processing and Control, 2022 - Elsevier
Abstract High-Resolution (HR) Magnetic Resonance Images (MRI) can help physician
diagnosis lesion more effectively. However, in practice, it is difficult to obtain HR-MRI due to …
diagnosis lesion more effectively. However, in practice, it is difficult to obtain HR-MRI due to …
[HTML][HTML] 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 …
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 …
A hybrid convolutional neural network for super‐resolution reconstruction of MR images
Y Zheng, B Zhen, A Chen, F Qi, X Hao, B Qiu - Medical physics, 2020 - Wiley Online Library
Purpose Spatial resolution is an important parameter for magnetic resonance imaging (MRI).
High‐resolution MR images provide detailed information and benefit subsequent image …
High‐resolution MR images provide detailed information and benefit subsequent image …
[HTML][HTML] FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution
High-resolution magnetic resonance images can provide fine-grained anatomical
information, but acquiring such data requires a long scanning time. In this paper, a …
information, but acquiring such data requires a long scanning time. In this paper, a …
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 …
[HTML][HTML] CNN-based superresolution reconstruction of 3D MR images using thick-slice scans
Due to inherent physical and hardware limitations, 3D MR images are often acquired in the
form of orthogonal thick slices, resulting in highly anisotropic voxels. This causes the partial …
form of orthogonal thick slices, resulting in highly anisotropic voxels. This causes the partial …