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Retrospective Motion Correction of Magnitude-Input MR Images

MPG-Autoren
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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;

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Schuler,  C
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, 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;

/persons/resource/persons84193

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

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Zitation

Loktyushin, A., Schuler, C., Scheffler, K., & Schölkopf, B. (2016). Retrospective Motion Correction of Magnitude-Input MR Images. In K. Bhatia (Ed.), Machine Learning Meets Medical Imaging (pp. 3-12). Piscataway, NJ, USA: IEEE.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-7AB4-C
Zusammenfassung
There has been a considerable progress recently in understanding and developing solutions to the problem of image quality deterioration due to patients’ motion in MR scanners. Retrospective methods can be applied to previously acquired motion corrupted data, however, such methods require complex-valued raw volumes as input. It is common practice, though, to preserve only spatial magnitudes of the medical scans, which makes the existing post-processing-based approaches inapplicable. In this work, we make first humble steps towards solving the problem of motion-related artifacts in magnitude-only scans. We propose a learning-based approach, which involves using large-scale convolutional neural networks to learn the transformation from motion-corrupted magnitude observations to the sharp images.