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Abstract #1514

Accelerated Regularized Image Reconstruction in Spatiotemporal MRI

Alexander Gutierrez1, Di Xiao1, Jarvis Haupt1, Albert Jang2, Steen Moeller2, and Michael Garwood2

1University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota

Interest in spatiotemporally-encoded MRI methods has increased over the last decade due mainly to their high tolerance to magnetic field inhomogeneities. However, the data acquired in spatiotemporal MRI can lead to challenging image reconstruction problems. In this abstract we propose a new framework for reconstructing images that leverages compressible structure in recent spatiotemporal encoding techniques to enable an iterative approximate inversion of the Bloch-equations for imaging. In particular, we can often obtain a visually indistinguishable reconstruction up to an order of magnitude faster than using the full inversion.

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