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Abstract #2273

Generalized ABSINTHE with Sparsity-Enforcing Regularization

Eric Y. Pierre1, Nicole Seiberlich1, Stephen Yutzy2, Vikas Gulani3, Mark A. Griswold, 13

1Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; 2Dept. of Radiology, University of Pittsburgh, PA, United States; 3Dept. of Radiology, University Hospitals of Cleveland, OH, United States

A general ABSINTHE framework that makes use of prior information to help reduce the amount of data needed to generate an image of a given object is presented. It can be used with any undersampled data reconstruction technique and with any form of prior information compatible with the acquisition scheme. Results are presented for an implementation using SENSE and a database of previously acquired images with identical coil configuration, identical resolution, similar anatomical positioning, and similar image contrast as the signal to reconstruct. A regularization criteria for reconstruction based on a priori knowledge was introduced which enforces sparsity.