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Abstract #4753

Non-linear Inverse Compressed-Sensing Reconstruction for Self-Gated Multidimensional Cardiac MRI: XD-NLINV

H. Christian M. Holme1,2, Sebastian Rosenzweig1,2, Xiaoqing Wang1,2, and Martin Uecker1,2

1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, 2partner site Göttingen, German Center for Cardiovascular Research (DZHK), Göttingen, Germany

Motion is a perpetual challenge in cardiac MRI: for comfortable free-breathing exams, both cardiac and breathing motion need to be resolved. Self-gating approaches have been proposed to automatically bin MRI data into appropriate motion states. Here, we propose a new combined parallel imaging/compressed sensing reconstruction for such multi-dimensional datasets. This method, termed XD-NLINV, solves the non-linear parallel imaging problem, simultaneously estimating images and coil sensitivities. This assures efficient use of the available data and removes the need for pre-calculating the coil profiles. We present initial results showing high image quality for self-gated cardiac short-axis data, resolving both breathing and cardiac motion.

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