Beyond traditional Magnetic Resonance processing with Artificial Intelligence

TL;DR

This paper demonstrates how AI-driven neural networks can address complex NMR signal processing challenges, surpassing traditional methods by enabling quadrature detection, uncertainty quantification, and reference-free spectrum quality assessment.

Contribution

Introduction of a new AI toolbox, MR-Ai, capable of solving three previously impossible NMR processing problems with neural networks.

Findings

AI enables quadrature detection from Echo/Anti-Echo signals

Uncertainty of signal intensity can be quantified at each spectrum point

A reference-free score for NMR spectrum quality assessment is proposed

Abstract

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.