Andr Fischer1,2, Nicole Seiberlich3, Mark A. Griswold3, Peter M. Jakob1,2, Felix A. Breuer1
1Research Center Magnetic Resonance Bavaria (MRB) e.V., Wuerzburg, Germany; 2Department of Experimental Physics 5, University of Wuerzburg, Wuerzburg, Germany; 3Department of Radiology, University Hospitals, Cleveland, OH, United States
Real-time imaging with high spatial and high temporal resolution is of great interest in cardiac imaging. Golden angle radial imaging allows to retrospectively selecting the temporal resolution by grouping a certain number of temporally adjacent projections to a timeframe. In this work, a Compressed Sensing based technique is presented to reconstruct a Golden angle radially undersampled real-time cardiac dataset. Thereby, the sparse differences of the individual timeframes to a temporally averaged composite image of the dataset were CS reconstructed. By exploiting the joint sparsity of the receiver array, accurate reconstructions of a dataset exhibiting PVCs could be obtained from 24 projections.