M Stich1, J A J Richter2, T Wech3, T A Bley3, R Ringler4, H Köstler3, A E Campbell-Washburn5. 1. Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany; Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA; X-Ray & Molecular Imaging Lab, Technical University Amberg-Weiden, Germany. Electronic address: manuel_stich@gmx.de. 2. Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany; Comprehensive Heart Failure Center Würzburg, Würzburg, Germany. 3. Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany. 4. X-Ray & Molecular Imaging Lab, Technical University Amberg-Weiden, Germany. 5. Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
Abstract
PURPOSE: The gradient system transfer function (GSTF) can be used to describe the dynamic gradient system and applied for trajectory correction in non-Cartesian MRI. This study compares the field camera and the phantom-based methods to measure the GSTF and implements a compensation for the difference in measurement dwell time. METHODS: The self-term GSTFs of a MR system were determined with two approaches: 1) using a dynamic field camera and 2) using a spherical phantom-based measurement with standard MR hardware. The phantom-based GSTF was convolved with a box function to compensate for the dwell time dependence of the measurement. The field camera and phantom-based GSTFs were used for trajectory prediction during retrospective image reconstruction of 3D wave-CAIPI phantom images. RESULTS: Differences in the GSTF magnitude response were observed between the two measurement methods. For the wave-CAIPI sequence, this led to deviations in the GSTF predicted trajectories of 4% compared to measured trajectories, and residual distortions in the reconstructed phantom images generated with the phantom-based GSTF. Following dwell-time compensation, deviations in the GSTF magnitudes, GSTF-predicted trajectories, and resulting image artifacts were eliminated (< 0.5% deviation in trajectories). CONCLUSION: With dwell time compensation, both the field camera and the phantom-based GSTF self-terms show negligible deviations and lead to strong artifact reduction when they are used for trajectory correction in image reconstruction.
PURPOSE: The gradient system transfer function (GSTF) can be used to describe the dynamic gradient system and applied for trajectory correction in non-Cartesian MRI. This study compares the field camera and the phantom-based methods to measure the GSTF and implements a compensation for the difference in measurement dwell time. METHODS: The self-term GSTFs of a MR system were determined with two approaches: 1) using a dynamic field camera and 2) using a spherical phantom-based measurement with standard MR hardware. The phantom-based GSTF was convolved with a box function to compensate for the dwell time dependence of the measurement. The field camera and phantom-based GSTFs were used for trajectory prediction during retrospective image reconstruction of 3D wave-CAIPI phantom images. RESULTS: Differences in the GSTF magnitude response were observed between the two measurement methods. For the wave-CAIPI sequence, this led to deviations in the GSTF predicted trajectories of 4% compared to measured trajectories, and residual distortions in the reconstructed phantom images generated with the phantom-based GSTF. Following dwell-time compensation, deviations in the GSTF magnitudes, GSTF-predicted trajectories, and resulting image artifacts were eliminated (< 0.5% deviation in trajectories). CONCLUSION: With dwell time compensation, both the field camera and the phantom-based GSTF self-terms show negligible deviations and lead to strong artifact reduction when they are used for trajectory correction in image reconstruction.
Keywords:
Dwell time compensation; Field camera; Gradient impulse response function; Gradient system transfer function; Trajectory correction; Wave Wave-CAIPI imaging
Authors: D C Peters; F R Korosec; T M Grist; W F Block; J E Holden; K K Vigen; C A Mistretta Journal: Magn Reson Med Date: 2000-01 Impact factor: 4.668
Authors: S Johanna Vannesjo; Nadine N Graedel; Lars Kasper; Simon Gross; Julia Busch; Maximilian Haeberlin; Christoph Barmet; Klaas P Pruessmann Journal: Magn Reson Med Date: 2015-07-27 Impact factor: 4.668
Authors: Berkin Bilgic; Borjan A Gagoski; Stephen F Cauley; Audrey P Fan; Jonathan R Polimeni; P Ellen Grant; Lawrence L Wald; Kawin Setsompop Journal: Magn Reson Med Date: 2014-07-01 Impact factor: 4.668
Authors: Manuel Stich; Tobias Wech; Anne Slawig; Ralf Ringler; Andrew Dewdney; Andreas Greiser; Gudrun Ruyters; Thorsten A Bley; Herbert Köstler Journal: Magn Reson Med Date: 2018-02-25 Impact factor: 4.668
Authors: Ethan K Brodsky; Jessica L Klaers; Alexey A Samsonov; Richard Kijowski; Walter F Block Journal: Magn Reson Med Date: 2012-04-05 Impact factor: 4.668