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Abstract #2919

CS-Dixon: Compressed Sensing for Water-Fat Dixon Reconstruction

Mariya Doneva1, Peter Brnert2, Holger Eggers2, Alfred Mertins1, John Pauly3, Michael Lustig3,4

1Institute for Signal Processing, University of Lbeck, Lbeck, Germany; 2Philips Research Europe, Hamburg, Germany; 3Electrical Engineering, Stanford University, CA, United States; 4Electrical Engineering, UC Berkeley, CA, United States

An integrated Compressed Sensing-Dixon algorithm is proposed, which applies a sparsity constraint on the water and fat images and jointly estimates water, fat and field map images. The method allows scan time reduction of above 3 in 3D MRI, fully compensating for the additional time necessary to acquire the chemical shift encoded data.