14 citations · 18 across the 7 of their papers we have counts for
7 papers · 1 filter
An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS
Meijin Lin, Lin Guo, Dicheng Chen +10
Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the…
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…
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…
Image Reconstruction with Low-rankness and Self-consistency of k-space Data in Parallel MRI
Xinlin Zhang, Di Guo, Yiman Huang +4
Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, al…
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Xiaobo Qu, Yihui Huang, Hengfa Lu +5
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept…