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Nassirpour, Sahar; Chang, Paul; Fillmer, Ariane; Henning, Anke

A comparison of optimization algorithms for localized in vivo B0 shimming

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  • Medientyp: E-Artikel
  • Titel: A comparison of optimization algorithms for localized in vivo B0 shimming
  • Beteiligte: Nassirpour, Sahar; Chang, Paul; Fillmer, Ariane; Henning, Anke
  • Erschienen: Wiley, 2018
  • Erschienen in: Magnetic Resonance in Medicine
  • Sprache: Englisch
  • DOI: 10.1002/mrm.26758
  • ISSN: 0740-3194; 1522-2594
  • Schlagwörter: Radiology, Nuclear Medicine and imaging
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:sec><jats:title>Purpose</jats:title><jats:p>To compare several different optimization algorithms currently used for localized in vivo B<jats:sub>0</jats:sub> shimming, and to introduce a novel, fast, and robust constrained regularized algorithm (<jats:italic>ConsTru</jats:italic>) for this purpose.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Ten different optimization algorithms (including samples from both generic and dedicated least‐squares solvers, and a novel constrained regularized inversion method) were implemented and compared for shimming in five different shimming volumes on 66 in vivo data sets from both 7 T and 9.4 T. The best algorithm was chosen to perform single‐voxel spectroscopy at 9.4 T in the frontal cortex of the brain on 10 volunteers.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The results of the performance tests proved that the shimming algorithm is prone to unstable solutions if it depends on the value of a starting point, and is not regularized to handle ill‐conditioned problems. The <jats:italic>ConsTru</jats:italic> algorithm proved to be the most robust, fast, and efficient algorithm among all of the chosen algorithms. It enabled acquisition of spectra of reproducible high quality in the frontal cortex at 9.4 T.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>For localized in vivo B<jats:sub>0</jats:sub> shimming, the use of a dedicated linear least‐squares solver instead of a generic nonlinear one is highly recommended. Among all of the linear solvers, the constrained regularized method (<jats:italic>ConsTru</jats:italic>) was found to be both fast and most robust. Magn Reson Med 79:1145–1156, 2018. © 2017 International Society for Magnetic Resonance in Medicine.</jats:p></jats:sec>