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Poster

Retrospective rigid motion correction of undersampled MRI data

MPS-Authors
/persons/resource/persons84372

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/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://www.ismrm.org/15/program_files/WedTP03.htm
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Citation

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., & Kober, T. (2015). Retrospective rigid motion correction of undersampled MRI data. Poster presented at 23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2015), Toronto, Canada.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-45F5-B
Abstract
The present study combines retrospective motion correction and GRAPPA reconstruction. We propose a technique that performs several alternations of GRAPPA interpolation and motion correction steps, suppressing the artifacts caused by motion over the course of the optimization. Motion parameters are estimated directly from the data with the aid of free induction decay navigators. The proposed algorithm does not require a priori knowledge of coil sensitivity profiles and can be applied retrospectively to data acquired with generic sequences such as MP-RAGE. The algorithm was tested on motion corrupted brain images of healthy volunteers, performing controlled head movement during the scan. Results demonstrate a significant improvement in image quality.