Bayesian deep learning-based 1H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout

Recently, deep learning showed its potential in the quantification of metabolites from ¹H-MRS brain spectra. However, previously used standard convolutional neural networks (CNNs) do not provide measurement uncertainty. We investigated the Bayesian CNNs (BCNNs) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation. The high correlations between the ground truth errors and the BCNN-predicted uncertainty for the majority of the metabolites found in this study may support the potential application of the proposed method in deep learning-based ¹H-MRS of the brain for metabolite quantification with simultaneous uncertainty estimation.