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 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 …
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 …
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 …
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 …
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 …
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 …
Deep MR brain image super-resolution using spatio-structural priors
High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In
practice, image resolution is restricted by factors like hardware and processing constraints …
practice, image resolution is restricted by factors like hardware and processing constraints …
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 …