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Abstract #1294

Novel Projection-Based Unsupervised Respiratory Motion Feedback for Free-Breathing Whole-Heart Coronary MR Imaging

Christoph Forman1, 2, Davide Piccini3, 4, Joachim Hornegger, 25, Michael O. Zenge6

1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nrnberg, Erlangen, Germany; 2Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-Universitt Erlangen-Nrnberg, Erlangen, Germany; 3Advanced Clinical Imaging Technology, Siemens Healthcare IM S AW, Lausanne, Switzerland; 4CIBM-AIT, Ecole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland; 5Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universitt Erlangen-Nrnberg, Erlangen, Germany; 6Healthcare Sector, Siemens AG, Erlangen, Germany

The experience of an extended number of volunteer experiments revealed that a respiratory motion detection by tracking the heart in SI projections can be degraded by several factors, e.g. suboptimal fat saturation, which may result in motion artefacts in the reconstructed images. By leaving the paradigm to correct for motion directly from the feedback of the projections behind, we propose a more robust method for the binning of the SI projections into respiratory phases. The robustness of the new approach was demonstrated in experiments with 10 healthy volunteers. The proposed method showed a high correlation to the conventional navigator.

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