CEST peaks are easy to detect at ultra-high-field strengths due to high signal and spectral separation. However, spectral coalescence and line broadening makes modeling of CEST effects at clinical field strengths (<=3T) a challenge. In this proof-of-concept study of super-resolution CEST imaging, the underlying spectral features of 3T Z-spectra were predicted using a neural network trained on 9.4T data. Applying the neural network to untrained volunteer and patient data acquired at 3T resulted in the expected contrast in healthy gray and white matter and tumor tissue in Z-spectra and APT, NOE, and MT CEST maps
The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and pre...
Using volumetric snapshot-GRE CEST MRI at 9.4T with high frequency sampling, we were able to separat...
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results...
CEST peaks are easy to detect at ultra-high-field strengths due to high signal and spectral separati...
Different neural network architectures for predicting 9T CEST contrasts from 3T spectral data are in...
Purpose To determine the feasibility of employing the prior knowledge of well‐separated chemical exc...
The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited...
Multi-Lorentzian analysis of chemical exchange saturation transfer (CEST) Z-spectra by non-linear le...
Spectral highly resolved Z-spectra in vivo are desirable for e.g. peak assignment in quantitative CE...
INTRODUCTION: To make 7T CEST MRI more available for radiologists, we developed a deepCEST pipeline ...
Purpose: In this work, we investigated the ability of neural networks to rapidly and robustly predic...
Introduction To make 7T CEST MRI more available for radiologists, we developed a deepCEST pipeline f...
Analysis of CEST data often requires complex mathematical modeling before contrast generation, which...
Model-based extraction of features, e.g. Lorentzian fitting of Z-spectra, in CEST MRI can be limited...
Analysis of chemical exchange saturation transfer (CEST) effects suffers from B0 inhomogeneity. Comm...
The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and pre...
Using volumetric snapshot-GRE CEST MRI at 9.4T with high frequency sampling, we were able to separat...
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results...
CEST peaks are easy to detect at ultra-high-field strengths due to high signal and spectral separati...
Different neural network architectures for predicting 9T CEST contrasts from 3T spectral data are in...
Purpose To determine the feasibility of employing the prior knowledge of well‐separated chemical exc...
The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited...
Multi-Lorentzian analysis of chemical exchange saturation transfer (CEST) Z-spectra by non-linear le...
Spectral highly resolved Z-spectra in vivo are desirable for e.g. peak assignment in quantitative CE...
INTRODUCTION: To make 7T CEST MRI more available for radiologists, we developed a deepCEST pipeline ...
Purpose: In this work, we investigated the ability of neural networks to rapidly and robustly predic...
Introduction To make 7T CEST MRI more available for radiologists, we developed a deepCEST pipeline f...
Analysis of CEST data often requires complex mathematical modeling before contrast generation, which...
Model-based extraction of features, e.g. Lorentzian fitting of Z-spectra, in CEST MRI can be limited...
Analysis of chemical exchange saturation transfer (CEST) effects suffers from B0 inhomogeneity. Comm...
The deepCEST approach enables to perform CEST experiments at a lower magnetic field strength and pre...
Using volumetric snapshot-GRE CEST MRI at 9.4T with high frequency sampling, we were able to separat...
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results...