TY  - JOUR
AU  - Becker, Moritz
AU  - Lehmkuhl, Sören
AU  - Kesselheim, Stefan
AU  - Korvink, Jan G.
AU  - Jouda, Mazin
TI  - Acquisitions with random shim values enhance AI-driven NMR shimming
JO  - Journal of magnetic resonance
VL  - 345
SN  - 1090-7807
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - FZJ-2023-00303
SP  - 107323 -
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 36375285
UR  - <Go to ISI:>//WOS:000900743900011
DO  - DOI:10.1016/j.jmr.2022.107323
UR  - https://juser.fz-juelich.de/record/917058
ER  -