114 citations · 354 across the 39 of their papers we have counts for
6 papers · 1 filter
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
Zi Wang, Di Guo, Zhangren Tu +9
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled expone…
Review and Prospect: NMR Spectroscopy Denoising & Reconstruction with Low Rank Hankel Matrices and Tensors
Tianyu Qiu, Zi Wang, Huiting Liu +2
Nuclear Magnetic Resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acqui…
Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-enhanced MRI
Yuhan Hu, Xinlin Zhang, Li Feng +6
Dynamic Contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensin…
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Yihui Huang, Jinkui Zhao, Zi Wang +3
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data ma…
An auto-parameter denoising method for nuclear magnetic resonance spectroscopy based on low-rank Hankel matrix
Tianyu Qiu, Wenjing Liao, Di Guo +4
Nuclear Magnetic Resonance (NMR) spectroscopy, which is modeled as the sum of damped exponential signals, has become an indispensable tool in various scenarios, such as the structu…
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
Dicheng Chen, Zi Wang, Di Guo +2
Since the concept of Deep Learning (DL) was formally proposed in 2006, it had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyz…