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| 001 | 917058 | ||
| 005 | 20230224084258.0 | ||
| 024 | 7 | _ | |a 10.1016/j.jmr.2022.107323 |2 doi |
| 024 | 7 | _ | |a 1090-7807 |2 ISSN |
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| 024 | 7 | _ | |a WOS:000900743900011 |2 WOS |
| 037 | _ | _ | |a FZJ-2023-00303 |
| 082 | _ | _ | |a 530 |
| 100 | 1 | _ | |a Becker, Moritz |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Acquisitions with random shim values enhance AI-driven NMR shimming |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2022 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Lehmkuhl, Sören |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Kesselheim, Stefan |0 P:(DE-Juel1)185654 |b 2 |
| 700 | 1 | _ | |a Korvink, Jan G. |0 P:(DE-HGF)0 |b 3 |e Corresponding author |
| 700 | 1 | _ | |a Jouda, Mazin |0 P:(DE-HGF)0 |b 4 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.jmr.2022.107323 |g Vol. 345, p. 107323 - |0 PERI:(DE-600)1469665-4 |p 107323 - |t Journal of magnetic resonance |v 345 |y 2022 |x 1090-7807 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/917058/files/2022_Becker_JMRO_enhancedDeepRegression-2.pdf |y Restricted |
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| 914 | 1 | _ | |y 2022 |
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