Clustering NMR: Machine learning assistive rapid (pseudo) two-dimensional relaxometry mapping

Abstract

Low-field nuclear magnetic resonance (NMR) relaxometry is an attractive approach for point-of-care testing medical diagnosis, industrial food science, and in situ oil-gas exploration. One of the problem however is, the inherently long relaxation time of the (liquid) sample, (and hence low signal-to-noise ratio) causes unnecessarily long repetition time. In this work, we present a new class of methodology for rapid and accurate object classification using NMR relaxometry with the aid of machine learning. We demonstrate that the sensitivity and specificity of the classification is substantially improved with higher order of (pseudo)-dimensionality (e.g., 2D or multidimensional). This new methodology (termed as Clustering NMR) is extremely useful for rapid and accurate object classification (in less than a minute) using the low-field NMR.