Clinical assessment of deep learning–based super-resolution for 3D volumetric brain MRI
JD Rudie, T Gleason, MJ Barkovich… - Radiology: Artificial …, 2022 - pubs.rsna.org
Artificial intelligence (AI)–based image enhancement has the potential to reduce scan times
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …
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 …
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 …
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 …
Deepvolume: Brain structure and spatial connection-aware network for brain mri super-resolution
Z Li, J Yu, Y Wang, H Zhou, H Yang… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical
structures and more precise clinical information than thick-section images. However, thin …
structures and more precise clinical information than thick-section images. However, thin …
[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 …
Brain MRI super-resolution using 3D dilated convolutional encoder–decoder network
The spatial resolution of magnetic resonance images (MRI) is limited by the hardware
capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep …
capacity, sampling time, signal-to-noise ratio (SNR), and patient comfort. Recently, deep …
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 …
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 …
SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning
High resolution magnetic resonance (MR) images are desired in many clinical and research
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …