Beyond traditional Magnetic Resonance processing with Artificial Intelligence
This work addresses specific bottlenecks in NMR data processing for researchers in chemistry and materials science, offering new AI-based solutions to previously unsolvable problems.
The study tackled three challenging NMR processing problems by developing a toolbox called MR-Ai with artificial neural networks, enabling quadrature detection from single modulation, uncertainty estimation in spectra, and reference-free quality scoring, demonstrating AI's potential to revolutionize NMR analysis.
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.