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 GAN and 3D multi-level DenseNet: smaller, faster, and better
High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical
information that is critical for diagnosis in the clinical application. However, HR MRI typically …
information that is critical for diagnosis in the clinical application. However, HR MRI typically …
Brain MRI super resolution using 3D deep densely connected neural networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical
information and is often necessary for accurate quantitative analysis. However, high spatial …
information and is often necessary for accurate quantitative analysis. However, high spatial …
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 …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
[HTML][HTML] SOUP-GAN: Super-resolution MRI using generative adversarial networks
There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …
research applications. Image quality is inevitably traded off with acquisition time, which in …
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 …
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 …
[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 …