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.