ArticleEJNMMI research2022
Prediction of multiple pH compartments by deep learning in magnetic resonance spectroscopy with hyperpolarized
Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Quo Vadis HyperpolarizedZeitschrift fur medizinische Physik · 2025Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHyperpolarization enhances the sensitivity of nuclear magnetic resonance experiments by between four and five orders of magnitude. Several hyperpolarized sensor molecules have been introduced that enable high sensitivity detection of metabolism and physiological parameters. However, hyperpolarized magnetic resonance spectroscopy imaging (MRSI) often suffers from poor signal-to-noise ratio and spectral analysis is complicated by peak overlap. Here, we study measurements of extracellular pH (pH
methodsWe investigate whether deep learning methods can yield improved pH
resultsComparing the networks' performances compartment-wise on a synthetic test data set and eight real kidney data shows superior performance of CNN compared to MLP and equal or superior performance compared to conventional line fitting. For correct prediction of real kidney pH
conclusionThe results of this study indicate that CNNs offer a reliable, accurate, fast and non-interactive method for analysis of hyperpolarized
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