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Abstract #2875

Iterative Approach to Atlas Based Sparsification of Image and Theoretical Estimation (Iterative ABSINTHE)

Eric Y. Pierre1, Nicole Seiberlich2, Stephen Yutzy1, Vikas Gulani2, Felix Breuer3, Mark Griswold2

1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; 2Departments of Radiology, Case Western Reserve University, Cleveland, OH, United States; 3Research Center Magnetic Resonance Bavaria e.V., Wrzburg, Germany

The ABSINTHE technique has been shown to allow better GRAPPA reconstructions at high undersampling factors by sparsifying the undersampled image to reconstruct. This study seeks to further increase the effectiveness of ABSINTHE by improving the PCA approximation which generates this sparse image. After a first standard ABSINTHE estimation, iterative ABSINTHE uses fully-sampled eigenvectors to generate an even sparser representation of the undersampled data. The efficacy of this technique for simulated data and longitudinal simulations is demonstrated, and an improved image quality is shown for iterative ABSINTHE in comparison to the standard ABSINTHE and GRAPPA techniques.