Beyond traditional Magnetic Resonance processing with Artificial Intelligence
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Abstract (arXiv)
Smart signal processing approaches using Artificial Intelligence are gaining momentum in NMR applications. In this study, we demonstrate that AI offers new opportunities beyond tasks addressed by traditional techniques. We developed and trained several artificial neural networks in our new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three "impossible" problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme; accessing uncertainty of signal intensity at each point in a spectrum processed by any given method; and defining a reference-free score for quantitative access of NMR spectrum quality. Our findings highlight the potential of AI techniques to revolutionize NMR processing and analysis.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 51 references that are in the map. First posted on arXiv 13 May 2024.
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7 of 51 references.
References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
- 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
- 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
- 2020Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopycited by 17
- 2019Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learningcited by 16
- 2022Resolution enhancement of NMR by decoupling with low-rank Hankel modelcited by 1
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