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

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

Abstract Magnetic Resonance Spectroscopy (MRS) and Spectroscopic Imaging (MRSI) are
non-invasive techniques to map tissue contents of many metabolites in situ in humans …

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 Absolute spectral quantification is the standard method for deriving estimates of the
concentration from metabolite signals measured using in vivo proton MRS (1H‐MRS). This …

Abstract Proton (1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool
capable of quantifying brain metabolite concentrations in vivo. Prioritization of …

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

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