Clustering NMR: Machine learning assistive rapid 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. However, one of the problems is the inherently long relaxation time of the (liquid) sample (and hence low signal-to-noise ratio) which causes unnecessarily long repetition time. In this work, a new methodology is presented for a rapid and accurate object classification using NMR relaxometry with the aid of machine learning techniques. It is demonstrated that the sensitivity and specificity of the classification are substantially improved with a higher order of (pseudo)-dimensionality (e.g., 2D or multidimensional). This new methodology (the so-called Clustering NMR) may be extremely useful for rapid and accurate object classification (in less than a minute) using the low-field NMR.