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A comparison of optimization algorithms for localized in vivo B 0 shimming
Author(s) -
Nassirpour Sahar,
Chang Paul,
Fillmer Ariane,
Henning Anke
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26758
Subject(s) - algorithm , solver , computer science , voxel , optimization algorithm , mathematics , mathematical optimization , artificial intelligence , programming language
Purpose To compare several different optimization algorithms currently used for localized in vivo B 0 shimming, and to introduce a novel, fast, and robust constrained regularized algorithm ( ConsTru ) for this purpose. Methods 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. Results 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 ConsTru 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. Conclusions For localized in vivo B 0 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 ( ConsTru ) was found to be both fast and most robust. Magn Reson Med 79:1145–1156, 2018. © 2017 International Society for Magnetic Resonance in Medicine.