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 …
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 …
[HTML][HTML] Gradient-guided convolutional neural network for MRI image super-resolution
X Du, Y He - Applied Sciences, 2019 - mdpi.com
Super-resolution (SR) technology is essential for improving image quality in magnetic
resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency …
resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency …
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 …
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 …
Pyramid orthogonal attention network based on dual self-similarity for accurate mr image super-resolution
For magnetic resonance (MR) images sharing visual characteristics, the internal structure
repetitions of different scales are considerable image-specific priors. Following the …
repetitions of different scales are considerable image-specific priors. Following the …
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 …
3D dense convolutional neural network for fast and accurate single MR image super-resolution
L Wang, J Du, A Gholipour, H Zhu, Z He… - … Medical Imaging and …, 2021 - Elsevier
Super-resolution (SR) MR image reconstruction has shown to be a very promising direction
to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we …
to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we …
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 …
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 …