English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Meeting Abstract

A Comparison of Optimization Algorithms for Localized in-vivo B0 Shimming

MPS-Authors
/persons/resource/persons192740

Nassirpour,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192839

Chang,  YC
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84402

Henning,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

Link
(Any fulltext)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Nassirpour, S., Chang, Y., Fillmer, A., & Henning, A. (2016). A Comparison of Optimization Algorithms for Localized in-vivo B0 Shimming. In 24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2016).


Cite as: https://hdl.handle.net/21.11116/0000-0000-7CC4-8
Abstract
This work presents a study on the performance of several least-squares optimization algorithms used for localized in-vivo B0 shimming. Seven different algorithms were tested in 4 different shim volumes in the brain: global shimming region, single slice, and single voxels in two different positions with 3rd order shimming at 7T. Each algorithm's robustness and convergence were tested against noisy inputs and different starting values. The results give an interesting overview of the properties of each algorithm and their applicability. The regularized iterative inversion algorithm proves to be the best algorithmic approach suited to this problem.