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@ARTICLE{Becker:917058,
      author       = {Becker, Moritz and Lehmkuhl, Sören and Kesselheim, Stefan
                      and Korvink, Jan G. and Jouda, Mazin},
      title        = {{A}cquisitions with random shim values enhance {AI}-driven
                      {NMR} shimming},
      journal      = {Journal of magnetic resonance},
      volume       = {345},
      issn         = {1090-7807},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2023-00303},
      pages        = {107323 -},
      year         = {2022},
      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.},
      cin          = {JSC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36375285},
      UT           = {WOS:000900743900011},
      doi          = {10.1016/j.jmr.2022.107323},
      url          = {https://juser.fz-juelich.de/record/917058},
}