114 citations · 351 across the 33 of their papers we have counts for
5 papers · 1 filter
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Zi Wang, Chen Qian, Di Guo +4
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutiona…
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
Xinlin Zhang, Hengfa Lu, Di Guo +5
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance…
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Yirong Zhou, Chen Qian, Yi Guo +6
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages i…
XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures
Di Guo, Zhangren Tu, Yi Guo +13
Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition ti…
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
Dicheng Chen, Wanqi Hu, Huiting Liu +13
Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of 1H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typi…