The temperature dependence of gradient system response characteristics. Issue 4 (8th October 2019)

• Record Type: Journal Article
• Authors: Stich, Manuel; Pfaff, Christiane; Wech, Tobias; Slawig, Anne; Ruyters, Gudrun; Dewdney, Andrew; Ringler, Ralf; Köstler, Herbert
• Is Part Of: Magnetic resonance in medicine. Volume 83:Issue 4(2020)
• Page Start: 1519
• Page End: 1527
• Publication Date: 2019-10-08
• Subjects: gradient impulse response function -- gradient system response -- gradient system transfer function -- temperature dependency -- thermal variation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28013
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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 B0 ‐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 B0 ‐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 B0 ‐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 B0 ‐cross‐terms can be modeled using a linear and convolution modeling approach based on the three main temperature sensor elements.