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 …
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 …
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 …
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 …
Gradual back-projection residual attention network for magnetic resonance image super-resolution
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and objective Magnetic Resonance Image (MRI) analysis can provide
anatomical examination of internal organs, which is helpful for diagnosis of the disease …
anatomical examination of internal organs, which is helpful for diagnosis of the disease …
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 …
MRI super-resolution with ensemble learning and complementary priors
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However,
due to the limitations in hardware, scan time, and throughput, it is often clinically challenging …
due to the limitations in hardware, scan time, and throughput, it is often clinically challenging …
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 …
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 …
MR image super-resolution with squeeze and excitation reasoning attention network
High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed
information for reliable diagnosis and quantitative image analyses. Deep convolutional …
information for reliable diagnosis and quantitative image analyses. Deep convolutional …