[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 …
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 …
Scan-specific generative neural network for MRI super-resolution reconstruction
The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high
spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time …
spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time …
Super-resolution of magnetic resonance images using Generative Adversarial Networks
Abstract Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …
How can we make GAN perform better in single medical image super-resolution? A lesion focused multi-scale approach
Single image super-resolution (SISR) is of great importance as a low-level computer vision
task. The fast development of Generative Adversarial Network (GAN) based deep learning …
task. The fast development of Generative Adversarial Network (GAN) based deep learning …
Inversesr: 3d brain mri super-resolution using a latent diffusion model
J Wang, J Levman, WHL Pinaya, PD Tudosiu… - … Conference on Medical …, 2023 - Springer
High-resolution (HR) MRI scans obtained from research-grade medical centers provide
precise information about imaged tissues. However, routine clinical MRI scans are typically …
precise information about imaged tissues. However, routine clinical MRI scans are typically …
Arbitrary scale super-resolution for medical images
Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-
resolution image. Currently, deep learning-based SISR approaches have been widely …
resolution image. Currently, deep learning-based SISR approaches have been widely …
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 …
Multi-contrast MRI super-resolution via a multi-stage integration network
Super-resolution (SR) plays a crucial role in improving the image quality of magnetic
resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear …
resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear …