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Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T

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Hagberg,  GE
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bause,  J
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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Ehses,  P
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Pohmann,  R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Shajan,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
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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Hagberg, G., Bause, J., Ethofer, T., Ehses, P., Dresler, T., Herbert, C., et al. (2017). Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T. NeuroImage, 144(Part A), 203-216. doi:10.1016/j.neuroimage.2016.09.047.


Cite as: http://hdl.handle.net/21.11116/0000-0000-C367-0
Abstract
Mapping of the longitudinal relaxation time (T1) with high accuracy and precision is central for neuroscientific and clinical research, since it opens up the possibility to obtain accurate brain tissue segmentation and gain myelin-related information. An ideal, quantitative method should enable whole brain coverage within a limited scan time yet allow for detailed sampling with sub-millimeter voxel sizes. The use of ultra-high magnetic fields is well suited for this purpose, however the inhomogeneous transmit field potentially hampers its use. In the present work, we conducted whole brain T1 mapping based on the MP2RAGE sequence at 9.4 T and explored potential pitfalls for automated tissue classification compared with 3 T. Data accuracy and T2-dependent variation of the adiabatic inversion efficiency were investigated by single slice T1 mapping with inversion recovery EPI measurements, quantitative T2 mapping using multi-echo techniques and simulations of the Bloch equations. We found that the prominent spatial variation of the transmit field at 9.4 T (yielding flip angles between 20 and 180 of nominal values) profoundly affected the result of image segmentation and T1 mapping. These effects could be mitigated by correcting for both flip angle and inversion efficiency deviations. Based on the corrected T1 maps, new, ‘flattened’, MP2RAGE contrast images were generated, that were no longer affected by variations of the transmit field. Unlike the uncorrected MP2RAGE contrast images acquired at 9.4 T, these flattened images yielded image segmentations comparable to 3 T, making bias-field correction prior to image segmentation and tissue classification unnecessary. In terms of the T1 estimates at high field, the proposed correction methods resulted in an improved precision, with test-retest variability below 1 and a coefficient-of-variation across 25 subjects below 3.