Clustering Nuclear Magnetic Resonance: Machine learning assistive rapid two-dimensional relaxometry mapping

Abstract

Low-field nuclear magnetic resonance (NMR) relaxometry is an attractive approach from point-of-care testing medical diagnosis to in situ oil-gas exploration. One of the problems, however, is the inherently long relaxation time of the (liquid) samples, (and hence low signal-to-noise ratio) which causes unnecessarily long repetition time. In this work, a new class of methodology is presented for rapid and accurate object classification using NMR relaxometry with the aid of machine learning. It is demonstrated that the sensitivity and specificity of the classification is substantially improved with higher order of (pseudo)-dimensionality (e.g., 2D- or multi-dimensional). This new methodology (termed as ‘Clustering NMR’) may be extremely useful for rapid and accurate object classification (in less than a minute) using low-field NMR.