One-dimensional deep low-rank and sparse network for accelerated MRI

Z Wang, C Qian, D Guo, H Sun, R Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning has shown astonishing performance in accelerated magnetic resonance
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …

IMJENSE: scan-specific implicit representation for joint coil sensitivity and image estimation in parallel MRI

R Feng, Q Wu, J Feng, H She, C Liu… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging
(MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an …

Undersampled MRI reconstruction based on spectral graph wavelet transform

J Lang, C Zhang, D Zhu - Computers in Biology and Medicine, 2023 - Elsevier
Compressed sensing magnetic resonance imaging (CS-MRI) has exhibited great potential
to accelerate magnetic resonance imaging if an image can be sparsely represented. How to …

One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction

Z Wang, X Yu, C Wang, W Chen, J Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its
radiation-free, comprehensive insights into the human body, facilitating medical diagnoses …

A faithful deep sensitivity estimation for accelerated magnetic resonance imaging

Z Wang, H Fang, C Qian, B Shi, L Bao… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from
prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts …

Fast and Calibrationless low-rank parallel imaging reconstruction through unrolled deep learning estimation of multi-channel spatial support maps

Z Yi, J Hu, Y Zhao, L Xiao, Y Liu… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Low-rank technique has emerged as a powerful calibrationless alternative for parallel
magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low …

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

Z Wang, M Xiao, Y Zhou, C Wang, N Wu, Y Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac
diagnosis. To enable fast imaging, the k-space data can be undersampled but the image …

Structured Low-Rank Tensor Completion for IoT Spatiotemporal High-resolution Sensing Data Reconstruction

X Zhang, J He, XA Pan, Y Chi… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Due to various restrictions, some Internet of Things (IoT) sensing layers can only deploy a
small number of sensor nodes for spatiotemporal low-resolution environmental information …

Bloch equation enables physics-informed neural network in parametric magnetic resonance imaging

Q Cai, L Zhu, J Zhou, C Qian, D Guo, X Qu - arXiv preprint arXiv …, 2023 - arxiv.org
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical
diagnosis. Beyond the common image structures, parametric imaging can provide the …

Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI

W Chen, S Wu, S Wang, Z Li, J Yang, H Yao… - Medical Image …, 2025 - Elsevier
Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissues
from different perspectives and has wide clinical applications. By utilizing the auxiliary …