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Autofocusing-based correction of B0 fluctuation-induced ghosting

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Loktyushin,  A
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83898

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

/persons/resource/persons84193

Schölkopf,  B
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84187

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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Citation

Loktyushin, A., Ehses, P., Schölkopf, B., & Scheffler, K. (2016). Autofocusing-based correction of B0 fluctuation-induced ghosting. Poster presented at 24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2016), Singapore.


Cite as: https://hdl.handle.net/21.11116/0000-0000-7BA7-A
Abstract
Long-TE gradient-echo images are prone to ghosting artifacts. Such degradation is primarily due to magnetic field variations caused by breathing or motion. The effect of these fluctuations amounts to different phase offsets in each acquired k-space line. A common remedy is to measure the problematic phase offsets using an extra non-phase-encoded scan before or after each imaging readout. In this work, we attempt to estimate the phase offsets directly from the raw image data by optimization-based search of phases that minimize an image distortion measure. This eliminates the need for any sequence modifications and additional scan time.