Review and prospect: deep learning in nuclear magnetic resonance spectroscopy
Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
Review and prospect: NMR spectroscopy denoising and reconstruction with low‐rank Hankel matrices and tensors
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 …
chemistry, biology, and life science, but it suffers from relatively low sensitivity and long …
Accelerated nuclear magnetic resonance spectroscopy with deep learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in
chemistry and biology but often suffers from long experimental times. We present a proof‐of …
chemistry and biology but often suffers from long experimental times. We present a proof‐of …
Vandermonde factorization of Hankel matrix for complex exponential signal recovery—Application in fast NMR spectroscopy
Many signals are modeled as a superposition of exponential functions in spectroscopy of
chemistry, biology, and medical imaging. This paper studies the problem of recovering …
chemistry, biology, and medical imaging. This paper studies the problem of recovering …
Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI
Parallel magnetic resonance imaging has served as an effective and widely adopted
technique for accelerating data collection. The advent of sparse sampling offers aggressive …
technique for accelerating data collection. The advent of sparse sampling offers aggressive …
A sparse model-inspired deep thresholding network for exponential signal reconstruction—Application in fast biological spectroscopy
The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but
requires sophisticated reconstruction algorithms. Faithful reconstruction from partially …
requires sophisticated reconstruction algorithms. Faithful reconstruction from partially …
Exponential signal reconstruction with deep Hankel matrix factorization
Exponential function is a basic form of temporal signals, and how to fast acquire this signal is
one of the fundamental problems and frontiers in signal processing. To achieve this goal …
one of the fundamental problems and frontiers in signal processing. To achieve this goal …
An automatic denoising method for NMR spectroscopy based on low-rank Hankel model
Nuclear magnetic resonance (NMR) spectroscopy, whose time domain data is modeled as
the sum of damped exponential signals, has become an indispensable tool in various …
the sum of damped exponential signals, has become an indispensable tool in various …
High‐resolution, 3D multi‐TE 1H MRSI using fast spatiospectral encoding and subspace imaging
Purpose To develop a novel method to achieve fast, high‐resolution, 3D multi‐TE 1H‐MRSI
of the brain. Methods A new multi‐TE MRSI acquisition strategy was developed that …
of the brain. Methods A new multi‐TE MRSI acquisition strategy was developed that …
Deep learning can accelerate and quantify simulated localized correlated spectroscopy
Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic
structures and concentrations of different chemicals in a biochemical sample of interest …
structures and concentrations of different chemicals in a biochemical sample of interest …