Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
Medical image super-resolution reconstruction algorithms based on deep learning: A survey
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
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 …
[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 …
Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution
Super-resolving medical images can help physicians in providing more accurate
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
[图书][B] Deep learning for medical image analysis
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …
academic and industry researchers and graduate students taking courses on machine …
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 …
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 …
Review and prospect: artificial intelligence in advanced medical imaging
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical
imaging. Recently, deep learning-based AI techniques have been actively investigated in …
imaging. Recently, deep learning-based AI techniques have been actively investigated in …
A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution
In recent years, deep learning super-resolution models for progressive reconstruction have
achieved great success. However, these models which refer to multi-resolution analysis …
achieved great success. However, these models which refer to multi-resolution analysis …