Magnetic resonance imaging (MRI) plays an important role in modern medic...
Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful
a...
Magnetic resonance imaging (MRI) is a principal radiological modality th...
Magnetic Resonance Spectroscopy (MRS) is an important non-invasive techn...
Soft-thresholding has been widely used in neural networks. Its basic net...
To explain day-to-day (DTD) route-choice behaviors and traffic dynamics
...
In the area of urban transportation networks, a growing number of day-to...
Implicit regularization is an important way to interpret neural networks...
Recent deep learning is superior in providing high-quality images and
ul...
In this work, we propose a Physics-Informed Deep Diffusion magnetic reso...
Deep learning has innovated the field of computational imaging. One of i...
Deep learning has shown astonishing performance in accelerated magnetic
...
Objective: Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool t...
Multi-dimensional NMR spectroscopy is an invaluable biophysical tool in
...
The problem of the effective prediction for large-scale spatio-temporal
...
Exponential functions are powerful tools to model signals in various
sce...
Since the concept of deep learning (DL) was formally proposed in 2006, i...
Magnetic resonance imaging has been widely applied in clinical diagnosis...
The boom of non-uniform sampling and compressed sensing techniques
drama...
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable...
Signals are generally modeled as a superposition of exponential function...
Compressed sensing has shown great potentials in accelerating magnetic
r...
Objective: Improve the reconstructed image with fast and multi-class
dic...
This paper explores robust recovery of a superposition of R distinct
com...
Compressed sensing has shown great potential in reducing data acquisitio...