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

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.