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Abstract #1160

Minimization of Respiratory Motion Artifacts for Whole-Heart Coronary MRI: A Combination of Self-Navigation and Weighted Compressed Sensing Reconstruction

Christoph Forman1, 2, Davide Piccini1, Jana Hutter1, 3, Robert Grimm1, Joachim Hornegger1, 3, Michael O. Zenge4

1Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany; 2Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany; 3 Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany; 4MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Germany


Recently, self-navigation techniques were introduced for 3D radial in the field of free-breathing, whole-heart coronary imaging. Radial trajectories intrinsically are insensitve for motion during data acquisition. However, Cartesian sampling is superior to radial trajectories in terms of SNR and hardware limitations, e.g. gradient delays. Thus, we propose the application of respiratory self-navigation on an incoherent, undersampled Cartesian trajectory. The effects of residual respiratory motion are minimized by a weighted data fidelity term, exploiting the information derived from the self-navigation, in iterative image reconstruction. In-vivo experiments were performed on four healthy volunteers and compared to navigator-gated acquisition.