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Restoration of EPI Susceptibility Distortion at 3 Tesla: Comparison of Fluid Registration with Phase-Encoding Gradient Reversion

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Imperati,  Davide L. U.
Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Tittgemeyer,  Marc
Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schäfer,  Andreas
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Tittgemeyer_ISMRM.pdf
(Any fulltext), 923KB

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Citation

Imperati, D. L. U., Tittgemeyer, M., Schäfer, A., & Möller, H. E. (2006). Restoration of EPI Susceptibility Distortion at 3 Tesla: Comparison of Fluid Registration with Phase-Encoding Gradient Reversion. Poster presented at 14th Scientific Meeting, International Society for Magnetic Resonance in Medicine (ISMRM), Seattle, WA, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-D621-1
Abstract
From mere visual inspection (Fig. 2) results obtained by fluid-dynamic image registration schemes appear to be better than those obtained using reversedgradient methods, because they account for large displacements and are able to correct for distortions in slice select gradient direction, too. A closer inspection of the (computed) distortion field revealed implausible reconstruction, primarily in read-out direction. This effect is not in accordance with susceptibility distortion on SE-EPI images, and requires to be taken under control. Since susceptibility distortions increase with field strength, we suppose that fluid-dynamic registration may be the better solution to estimate the distortion field at 3 T or above, especially for high-resolution imaging. It may be interesting to modify fluid-dynamic image registration to implement a fluid-dynamic regularization for reversed-gradient methods to unify the advantages of a reconstruction based on the information from both images and the feature of the fluid registration to account for large displacements.