Zi Wang

Orcid: 0000-0001-8635-8334

Affiliations:
  • Xiamen University, National Institute for Data Science in Health and Medicine, Department of Electronic Science, China


According to our database1, Zi Wang authored at least 29 papers between 2020 and 2026.

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Bibliography

2026
Diffusion-prior based implicit neural representation for arbitrary-scale cardiac cine MRI super-resolution.
Inf. Fusion, 2026

2025
DRIMV_TSK: An Interpretable Surgical Evaluation Model for Incomplete Multi-View Rectal Cancer Data.
CoRR, June, 2025

From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction.
CoRR, June, 2025

One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction.
Medical Image Anal., 2025

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.
Medical Image Anal., 2025

2024
MRI reconstruction with enhanced self-similarity using graph convolutional network.
BMC Medical Imaging, December, 2024

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.
IEEE J. Biomed. Health Informatics, April, 2024

CloudBrain-ReconAI: A Cloud Computing Platform for MRI Reconstruction and Radiologists' Image Quality Evaluation.
IEEE Trans. Cloud Comput., 2024

CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI.
CoRR, 2024

Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI.
CoRR, 2024

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.
CoRR, 2024

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction.
Proceedings of the IEEE International Conference on Image Processing, 2024

2023
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging.
IEEE Trans. Biomed. Eng., December, 2023

A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction - Application in Fast Biological Spectroscopy.
IEEE Trans. Neural Networks Learn. Syst., October, 2023

Exponential Signal Reconstruction With Deep Hankel Matrix Factorization.
IEEE Trans. Neural Networks Learn. Syst., September, 2023

Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence.
IEEE Signal Process. Mag., March, 2023

One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI.
IEEE Trans. Medical Imaging, 2023

XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures.
IEEE Trans. Computational Imaging, 2023

Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data.
IEEE Trans. Computational Imaging, 2023

A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction.
CoRR, 2023

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction.
CoRR, 2023

Physics-informed Deep Diffusion MRI Reconstruction: Break the Bottleneck of Training Data in Artificial Intelligence.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance.
CoRR, 2022

2021
A partial sum of singular-value-based reconstruction method for non-uniformly sampled NMR spectroscopy.
IET Signal Process., 2021

A review on deep learning MRI reconstruction without fully sampled k-space.
BMC Medical Imaging, 2021

XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

Accelerated image reconstruction with separable Hankel regularization in parallel MRI.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020
Accelerated NMR Spectroscopy: Merge Optimization with Deep Learning.
CoRR, 2020

Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy.
CoRR, 2020


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