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Gradient waveform pre‐emphasis based on the gradient system transfer function
Author(s) -
Stich Manuel,
Wech Tobias,
Slawig Anne,
Ringler Ralf,
Dewdney Andrew,
Greiser Andreas,
Ruyters Gudrun,
Bley Thorsten A.,
Köstler Herbert
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.27147
Subject(s) - emphasis (telecommunications) , trajectory , waveform , computer science , imaging phantom , transfer function , optical transfer function , iterative reconstruction , artificial intelligence , physics , optics , telecommunications , radar , electrical engineering , astronomy , engineering
Purpose The gradient system transfer function (GSTF) has been used to describe the distorted k‐space trajectory for image reconstruction. The purpose of this work was to use the GSTF to determine the pre‐emphasis for an undistorted gradient output and intended k‐space trajectory. Methods The GSTF of the MR system was determined using only standard MR hardware without special equipment such as field probes or a field camera. The GSTF was used for trajectory prediction in image reconstruction and for a gradient waveform pre‐emphasis. As test sequences, a gradient‐echo sequence with phase‐encoding gradient modulation and a gradient‐echo sequence with a spiral read‐out trajectory were implemented and subsequently applied on a structural phantom and in vivo head measurements. Results Image artifacts were successfully suppressed by applying the GSTF‐based pre‐emphasis. Equivalent results are achieved with images acquired using GSTF‐based post‐correction of the trajectory as a part of image reconstruction. In contrast, the pre‐emphasis approach allows reconstruction using the initially intended trajectory. Conclusion The artifact suppression shown for two sequences demonstrates that the GSTF can serve for a novel pre‐emphasis. A pre‐emphasis based on the GSTF information can be applied to any arbitrary sequence type.