Journal Article

FZJ-2023-00303

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Acquisitions with random shim values enhance AI-driven NMR shimming

Becker, M. ; Lehmkuhl, S. ; Kesselheim, S.FZJ* ; Korvink, J. G. (Corresponding author) ; Jouda, M. (Corresponding author)

2022
Elsevier Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.jmr.2022.107323

Abstract: Shimming is still an unavoidable, time-consuming and cumbersome burden that precedes NMR experiments, and aims to achieve a homogeneous magnetic field distribution, which is required for expressive spectroscopy measurements. This study presents multiple enhancements to AI-driven shimming. We achieve fast, quasi-iterative shimming on multiple shims simultaneously via a temporal history that combines spectra and past shim actions. Moreover, we enable efficient data collection by randomized dataset acquisition, allowing scalability to higher-order shims. Application at a low-field benchtop magnet reduces the linewidth in 87 of 100 random distortions from 4 Hz to below 1 Hz, within less than 10 NMR acquisitions. Compared to, and combined with, traditional methods, we significantly enhance both the speed and performance of shimming algorithms. In particular, AI-driven shimming needs roughly 1/3 acquisitions, and helps to avoid local minima in of the cases. Our dataset and code is publicly available.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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