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Abstract #2828

Auto-Calibration Method for K-T-SENSE: Using TGRAPPA to Calculate the Training Data

Paola Irene Ponce1, Martin Blaimer2, Felix A. Breuer2, Peter M. Jakob1, Mark A. Griswold3, Peter Kellman4

1Department of Experimental Physics 5, University of Wrzburg, Wrzburg, Bavaria, Germany; 2Research Center Magnetic Resonance Bavaria (MRB), Wrzburg, Bavaria, Germany; 3Department of Radiology, University Hospitals of Cleveland and Case Western Reserve University, Cleveland, OH, USA; 4Laboratory of Cardiac Energetics, National Institutes of Health, National Heart, Lung and Blood Institute, Bethesda, MD, USA

In accelerated dynamic MRI, several methods have been proposed to reconstruct missing data based on prior knowledge about the motion. The k-t SENSE method utilizes coil sensitivity variations and correlations in k-space and time to reconstruct missing data. However, k-t SENSE requires additional training data as prior information about the dynamics of the object. This work removes the requirement of an additional training data acquisition by employing additional TGRAPPA reconstructions on the undersampled dataset. TGRAPPA produces images with high spatial and temporal resolution without temporal filtering effects and is therefore fully applicable for producing high-quality training data for k-t SENSE.