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000917058 0247_ $$2doi$$a10.1016/j.jmr.2022.107323
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000917058 0247_ $$2ISSN$$a0022-2364
000917058 0247_ $$2ISSN$$a1096-0856
000917058 0247_ $$2ISSN$$a1557-8968
000917058 0247_ $$2pmid$$a36375285
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000917058 037__ $$aFZJ-2023-00303
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000917058 1001_ $$0P:(DE-HGF)0$$aBecker, Moritz$$b0
000917058 245__ $$aAcquisitions with random shim values enhance AI-driven NMR shimming
000917058 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2022
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000917058 520__ $$aShimming is still an unavoidable, time-consuming and cumbersome burden that precedes NMR experiments, and aims to achieve a homogeneous magnetic field distribution, which is required for expressive spectroscopy measurements. This study presents multiple enhancements to AI-driven shimming. We achieve fast, quasi-iterative shimming on multiple shims simultaneously via a temporal history that combines spectra and past shim actions. Moreover, we enable efficient data collection by randomized dataset acquisition, allowing scalability to higher-order shims. Application at a low-field benchtop magnet reduces the linewidth in 87 of 100 random distortions from  4 Hz to below 1 Hz, within less than 10 NMR acquisitions. Compared to, and combined with, traditional methods, we significantly enhance both the speed and performance of shimming algorithms. In particular, AI-driven shimming needs roughly 1/3 acquisitions, and helps to avoid local minima in  of the cases. Our dataset and code is publicly available.
000917058 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
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000917058 7001_ $$0P:(DE-HGF)0$$aLehmkuhl, Sören$$b1
000917058 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b2
000917058 7001_ $$0P:(DE-HGF)0$$aKorvink, Jan G.$$b3$$eCorresponding author
000917058 7001_ $$0P:(DE-HGF)0$$aJouda, Mazin$$b4$$eCorresponding author
000917058 773__ $$0PERI:(DE-600)1469665-4$$a10.1016/j.jmr.2022.107323$$gVol. 345, p. 107323 -$$p107323 -$$tJournal of magnetic resonance$$v345$$x1090-7807$$y2022
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000917058 9141_ $$y2022
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