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 …
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 …
Single MR image super-resolution via channel splitting and serial fusion network
X Zhao, Y Zhang, Y Qin, Q Wang, T Zhang… - Knowledge-Based Systems, 2022 - Elsevier
In magnetic resonance imaging (MRI), spatial resolution is an important and critical imaging
parameter that represents how much information is contained in a unit space. Acquiring high …
parameter that represents how much information is contained in a unit space. Acquiring high …
Image super-resolution for MRI images using 3D faster super-resolution convolutional neural network architecture
V Mane, S Jadhav, P Lal - ITM Web of Conferences, 2020 - itm-conferences.org
Single image super-resolution using deep learning techniques has shown very high
reconstruction performance over the last few years. We propose a novel three-dimensional …
reconstruction performance over the last few years. We propose a novel three-dimensional …
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 …
Multiscale brain MRI super-resolution using deep 3D convolutional networks
CH Pham, C Tor-Díez, H Meunier, N Bednarek… - … Medical Imaging and …, 2019 - Elsevier
The purpose of super-resolution approaches is to overcome the hardware limitations and
the clinical requirements of imaging procedures by reconstructing high-resolution images …
the clinical requirements of imaging procedures by reconstructing high-resolution images …
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 …
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 …