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The temperature dependence of gradient system response characteristics
Author(s) -
Stich Manuel,
Pfaff Christiane,
Wech Tobias,
Slawig Anne,
Ruyters Gudrun,
Dewdney Andrew,
Ringler Ralf,
Köstler Herbert
Publication year - 2020
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.28013
Subject(s) - temperature gradient , scanner , convolution (computer science) , electromagnetic coil , imaging phantom , transfer function , relaxation (psychology) , nuclear magnetic resonance , materials science , mathematical analysis , physics , mathematics , computer science , optics , engineering , electrical engineering , quantum mechanics , artificial neural network , psychology , social psychology , machine learning
Purpose The gradient system transfer function (GSTF) characterizes the frequency transfer behavior of a dynamic gradient system and can be used to correct non‐Cartesian k‐space trajectories. This study analyzes the impact of the gradient coil temperature of a 3T scanner on the GSTF. Methods GSTF self‐ and B 0 ‐cross‐terms were acquired for a 3T Siemens scanner (Siemens Healthcare, Erlangen, Germany) using a phantom‐based measurement technique. The GSTF terms were measured for various temperature states up to 45°C. The gradient coil temperatures were measured continuously utilizing 12 temperature sensors which are integrated by the vendor. Different modeling approaches were applied and compared. Results The self‐terms depend linearly on temperature, whereas the B 0 ‐cross‐term does not. Effects induced by thermal variation are negligible for the phase response. The self‐terms are best represented by a linear model including the three gradient coil sensors that showed the maximum temperature dependence for the three axes. The use of time derivatives of the temperature did not lead to an improvement of the model. The B 0 ‐cross‐terms can be modeled by a convolution model which considers coil‐specific heat transportation. Conclusion The temperature dependency of the GSTF was analyzed for a 3T Siemens scanner. The self‐ and B 0 ‐cross‐terms can be modeled using a linear and convolution modeling approach based on the three main temperature sensor elements.