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Abstract #2867

Title: Reconstruction of Sparsely-Sampled Dynamic MRI Data Using Iterative Error Energy [1] Reduction

Sumati Krishnan1, David Moratal2, Lei-Hou Hamilton3, Senthil Ramamurthy4, Marijn Eduard Brummer4

1Emory University, Atlanta, GA, United States; 22Universitat Politcnica de Valncia, Valencia, Spain; 3Georgia Institute of Technology, Atlanta, GA, United States; 4Emory University, Atlanta, GA, United States

A well-known reconstruction method, based on error energy reduction [1], is adapted to sparsely sampled dynamic cardiac MRI. Inherent temporally band-limited properties of known static regions in the FOV, are used to recover additional resolution from information embedded in the acquired k-t samples. The algorithm converges as the error due to residual dynamic content in the static region is minimized. Reconstructions equivalent to direct matrix-inversion [2] are achieved with significantly reduced computational costs, while convergence properties are related to the sampling patterns. The proposed iterative method has potential applications for a variety of non-Cartesian grids as well as sparse-sampling patterns.