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Abstract #0554

Low Rank Compressed Sensing Reconstruction for More Precise Cardiac MRF Measurements

Jesse Ian Hamilton1, Yun Jiang1, Dan Ma2, Yong Chen2, Shivani Pawha2, Wei-Ching Lo1, Joshua Batesole2, Mark Griswold1,2, and Nicole Seiberlich1

1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, University Hospitals, Cleveland, OH, United States

A low rank compressed sensing and parallel imaging reconstruction termed Sparse MRF is introduced to improve the precision of mapping myocardial T1 and T2 with MR Fingerprinting. Sparse MRF enforces data consistency while also constraining the temporal signal evolutions using a low dimensional subspace derived from the SVD of the dictionary along time. Different reconstruction parameters are investigated in simulations with a cardiac phantom. Results from phantom and in vivo cardiac scans indicate that Sparse MRF yields approximately the same mean T1 and T2 measurements as other MRF matching techniques but with smaller standard deviations.

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