Applications of a deep learning method for anti-aliasing and super-resolution in MRI
Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios
(SNRs) are desired in many clinical and research applications. However, acquiring such …
(SNRs) are desired in many clinical and research applications. However, acquiring such …
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 …
Image super-resolution using progressive generative adversarial networks for medical image analysis
D Mahapatra, B Bozorgtabar, R Garnavi - Computerized Medical Imaging …, 2019 - Elsevier
Anatomical landmark segmentation and pathology localisation are important steps in
automated analysis of medical images. They are particularly challenging when the anatomy …
automated analysis of medical images. They are particularly challenging when the anatomy …
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 …
Review of data science trends and issues in porous media research with a focus on image‐based techniques
Data science as a flourishing interdisciplinary domain of computer and mathematical
sciences is playing an important role in guiding the porous material research streams. In the …
sciences is playing an important role in guiding the porous material research streams. In the …
[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 …
Multi-input cardiac image super-resolution using convolutional neural networks
Abstract 3D cardiac MR imaging enables accurate analysis of cardiac morphology and
physiology. However, due to the requirements for long acquisition and breath-hold, the …
physiology. However, due to the requirements for long acquisition and breath-hold, the …
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 …
LRTV: MR image super-resolution with low-rank and total variation regularizations
Image super-resolution (SR) aims to recover high-resolution images from their low-
resolution counterparts for improving image analysis and visualization. Interpolation …
resolution counterparts for improving image analysis and visualization. Interpolation …
Multi-contrast super-resolution MRI through a progressive network
Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided
therapy, and scientific research. A significant advantage of MRI over other imaging …
therapy, and scientific research. A significant advantage of MRI over other imaging …