Sub-volume motion detection to speed up image-based navigators and prospective motion correction
Anja Jäger1,2, Thomas Beck2, and Andreas Maier1
1Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Germany, 2Siemens Healthcare, MR Application Development, Erlangen, Germany
for detection of patient motion based on sub-volumes is presented. Current
methods for image-based motion detection are limited because rigid motion
parameters can only be detected for full volumes. This
limits the potential of navigator acceleration and causes undesirable effects
due to respiratory motion in some applications. Our novel approach extends the
rigid-body-motion model by detection based on a subset of slices relative to a
fully sampled reference volume. It is validated with phantom and in-vivo data
and allows for both considerable acceleration of navigator scans and
prospective correction of head motion in fMRI applications.
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