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Clustering NMR: Machine learning assistive rapid two-dimensional relaxometry mapping
  • Weng Kung Peng
Weng Kung Peng
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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.

Peer review status:Published

13 May 2020Submitted to Engineering Reports
14 May 2020Submission Checks Completed
14 May 2020Assigned to Editor
14 May 2020Reviewer(s) Assigned
16 Jun 2020Editorial Decision: Reject & Resub
31 Dec 20201st Revision Received
04 Jan 2021Submission Checks Completed
04 Jan 2021Assigned to Editor
13 Jan 2021Reviewer(s) Assigned
04 Feb 2021Editorial Decision: Revise Minor
08 Feb 20212nd Revision Received
08 Feb 2021Submission Checks Completed
08 Feb 2021Assigned to Editor
08 Feb 2021Editorial Decision: Accept
28 Feb 2021Published in Engineering Reports. 10.1002/eng2.12383