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Abstract #4554

Atlas Based Sparsification of Image and Theoretical Estimation (ABSINTHE)

Eric Pierre1, Nicole Seiberlich2, Stephen Yutzy1, Jean Tkach2,3, Mark Griswold2,3

1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; 2Radiology, Case Western Reserve University; 3University Hospitals of Cleveland

In GRAPPA, higher acceleration factors can be achieved with less noise enhancement when reconstructing sparse images. In this study, the ABSINTHE technique has been developed to render undersampled in vivo brain images sparser by removing the normal brain information using Principal Component Analysis with a training set of similar brain images. The resulting undersampled sparse images are then reconstructed using GRAPPA. The effectiveness of ABSINTHE for reconstructing undersampled simulated and in-vivo data is demonstrated, and an improved image quality in terms of lower artifact powers is shown for ABSINTHE in comparison to the standard GRAPPA technique.