Abstract #2556
Retrospective rigid motion correction of undersampled MRI data
Alexander Loktyushin 1 , Maryna Babayeva 2,3 , Daniel Gallichan 4 , Gunnar Krueger 2,3 , Klaus Scheffler 5,6 , and Tobias Kober 2,3
1
Empirical Inference, Max Planck Institute
for Intelligent Systems, Tbingen, Germany,
2
Siemens
ACIT - CHUV Radiology, Siemens Healthcare IM BM PI, &
Department of Radiology, University Hospital (CHUV),
Lausanne, Switzerland,
3
LTS5,
cole Polytechnique Fdrale de Lausanne, Lausanne,
Switzerland,
4
CIBM,
cole Polytechnique Fdrale de Lausanne, Lausanne,
Switzerland,
5
High-Field
Magnetic Resonance Center, Max Planck Institute for
Biological Cybernetics, Tbingen, Germany,
6
Department
for Biomedical Magnetic Resonance, University of
Tbingen, Tbingen, Germany
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.
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