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

Journal Article · 27 February 2021 · Engineering Reports

DOI: https://doi.org/10.1002/eng2.12383 · OSTI ID:1768115

Peng, Weng Kung  ^[1]

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1. Precision Medicine – Engineering Group International Iberian Nanotechnology Laboratory Braga Portugal

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.

• Sponsoring Organization: USDOE
• OSTI ID: 1768115
• Journal Information: Engineering Reports, Journal Name: Engineering Reports Journal Issue: 10 Vol. 3; ISSN 2577-8196
• Publisher: Wiley Blackwell (John Wiley & Sons)Copyright Statement
• Country of Publication: United States
• Language: English