Purpose While the recommended analysis method for magnetic resonance spectroscopy
data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach …

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research
and it has the unique capability to give a non-invasive access to the biochemical content …

Abstract Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics
profiles within biofluids or tissues and contributes to personalized treatments in medical …

Quantification of metabolites from magnetic resonance spectra (MRS) has many
applications in medicine and psychology, but remains a challenging task despite …

Purpose To develop a robust method for brain metabolite quantification in proton magnetic
resonance spectroscopy (1H‐MRS) using a convolutional neural network (CNN) that maps …

Purpose The aim of this study was to develop a method for metabolite quantification with
simultaneous measurement uncertainty estimation in deep learning‐based proton magnetic …

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for
biomedical detection. However, it is challenging to accurately quantify metabolites with …

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for
diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites …

Abstract Magnetic Resonance Spectroscopic Imaging (MRSI) is a clinical imaging modality
for measuring tissue metabolite levels in-vivo. An accurate estimation of spectral parameters …

Purpose To propose a novel end‐to‐end deep learning model to quantify absolute
metabolite concentrations from in vivo J‐point resolved spectroscopy (JPRESS) without …