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 …

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 …

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

Purpose To investigate the utility of an advanced magnetic resonance spectroscopy (MRS)
protocol in the clinical setting, and to compare the localization accuracy, spectral quality, and …

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

Purpose Accurate and precise MRS fitting is crucial for metabolite concentration
quantification of 1H‐MRS spectra. LCModel, a spectral fitting software, has shown to have …

Purpose To introduce a study design and statistical analysis framework to assess the
repeatability, reproducibility, and minimal detectable changes (MDCs) of metabolite …

Purpose To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo
dropout sampling for metabolite quantification with simultaneous uncertainty estimation in …

It has been shown previously that when LCModel is used for spectral quantification of short-
TE MRS data, increasing spectral linewidth results in a decrease in the estimated …