English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. View item / 
  2. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Poster

Fast Method for Parametric System Identification of Gradient Systems

MPS-Authors
/persons/resource/persons192839

Chang,  P
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133452

Eschelbach,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133464

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

/persons/resource/persons84187

Scheffler,  K       
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84402

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

External Resource

https://www.ismrm.org/14/program_files/EP17.htm
(Abstract)

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

Chang, P., Eschelbach, M., Avdievich, N., Scheffler, K., & Henning, A. (2014). Fast Method for Parametric System Identification of Gradient Systems. Poster presented at Joint Annual Meeting ISMRM-ESMRMB 2014, Milano, Italy.


Cite as: https://hdl.handle.net/21.11116/0000-0001-32DF-C
Abstract
A method for characterising a gradient system is introduced. This is a parametric method and thus the system has an analytic form. It is fast and requires only one measurement in each gradient direction and can thus be completed in minutes. It can also be extended to characterise the shim system. Monitoring the B0 field is done using a 16 channel field camera. The predicted model is compared to measured data.