Accurate determination of peak position is challenging for spectra with dense spectral regions paired with low SNR as occuring in pH measurements using hyperpolarized [1,5-13C2,3,6,6,6-D4]zymonic acid in kidney of mice. Despite scarcity of available data from preclinical experiments, convolutional neural networks (CNN) and multilayer perceptrons (MLP) could be trained by complementing real and augmented data with synthetic spectra. While MLPs do not achieve suitable performance, CNNs predict pH compartments with an accuracy comparable or superior to supervised line fitting in synthetic test spectra. Further, CNNs allow generation of composite pH maps with improved quality while quantitatively agreeing with line-fitted maps.
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