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Abstract #4627

Cramér-Rao Bound Optimized Linear Bases for Low-Rank Subspace Reconstruction

Andrew Mao1,2,3, Sebastian Flassbeck1,2, Cem Gultekin4, and Jakob Asslaender1,2
1Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY, United States, 3Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, United States, 4Courant Institute of Mathematical Sciences, New York University, New York, NY, United States

Synopsis

Keywords: Sparse & Low-Rank Models, Magnetization transfer, MR Fingerprinting, Hybrid State, Cramer-Rao bound, Quantitative Imaging, Low-Rank ReconstructionThis works extends the traditional framework for estimating low-rank bases that maximize preserved signal energy to additionally preserve the Cramér-Rao bound of the biophysical parameters in quantitative imaging. To this end, we orthogonalize the signal's derivatives wrt. the model parameters and incorporate them into the basis estimation process. We demonstrate in silico an improvement in the Cramér-Rao bound of all biophysical parameters with negligible cost to signal energy preservation, which translates to improved image quality and SNR in vivo.

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