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Comparison of the ultimate intrinsic SNR in a spherical phantom vs a realisitc human head model at 9.4 T

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

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

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Pfrommer, A., & Henning, A. (2016). Comparison of the ultimate intrinsic SNR in a spherical phantom vs a realisitc human head model at 9.4 T. Magnetic Resonance Materials in Physics, Biology and Medicine, 29(Supplement 1), S308-S309.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7C21-0
Abstract
Purpose/Introduction: To have a performance measure for RF receive coils at ultra-high field strength the ultimate intrinsic signal to-noise ratio (UISNR) was first calculated within a simple spherical model (1–3). Recently with the advance of ultrafast volume integral solvers (4) the UISNR could also be evaluated within a realistic human body model (5, 6). In this study we compared the UISNR obtained in a spherical phantom versus a realistic human head model. Subjects and Methods: We used the voxel model Duke with 5 mm isotropic resolution (7). The homogeneous spherical phantom had a radius of 10.3 cm with relative permittivity of 40 and conductivity of 0.6 S/m. For both setups (Fig 1) a generic surface current with curlfree and divergence-free patterns was running on a spherical surface of radius 14 cm. Thereby a basis set of vector spherical harmonics was used with expansion order of 60. The electromagnetic fields in Duke created by the basis current set were obtained with MARIE (8), following the procedure described in (6). For the spherical phantom the electromagnetic field problem was solved with dyadic Green’s functions (9) in a similar way as in (3). Results: In Fig. 2 we show the spatial distribution of the unaccelerated UISNR for Duke and the spherical phantom. In both models the maximum UISNR is located in the periphery. Regarding the spherical model the UISNR in the center is about 30 dB lower than in the periphery. The SNR damping in Duke is more than 40 dB in the midbrain and cerebellum and about 30 dB averaged over grey and white matter. Additionally we examined parallel imaging performance and plotted exemplary g-factor maps for 5 9 5 acceleration in LR- and AP direction (Fig. 2 bottom). The maximum g-factor occurring in the sphere is 1.68 as opposed to 1.25 in Duke, corresponding to an overestimation of 34 . In Fig. 3 we visualized the contribution of loop-like current patterns to the UISNR, which is complemented by dipole-like current patterns to reach the maximum possible SNR. If loops are the only receiving elements, for the spherical phantom it is possible to gain the total SNR in the center whereas only 67 are reached in Duke’s midbrain. Discussion/Conclusion: The comparison of the UISNR in a realistic human head model with a simple spherical phantom revealed significant differences. As RF arrays are built for the human body the UISNR should consequently be evaluated in a body model.