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 …
Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Image reconstruction from undersampled k-space data has been playing an important role
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning
C Duan, H Deng, S Xiao, J Xie, H Li, X Zhao, D Han… - European …, 2022 - Springer
Objectives Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and
quantitative assessment of lung morphometry, but its long acquisition time is not well …
quantitative assessment of lung morphometry, but its long acquisition time is not well …
Review and prospect: deep learning in nuclear magnetic resonance spectroscopy
Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping
Purpose To develop and evaluate a novel deep learning‐based image reconstruction
approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space …
approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space …
One-dimensional deep low-rank and sparse network for accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
Deep MRI reconstruction: unrolled optimization algorithms meet neural networks
Image reconstruction from undersampled k-space data has been playing an important role
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …
Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends
Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time (T1),
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …