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Abstract #3747

A Fast Approximation of Undersampling Artifacts in MR Fingerprinting

Debra McGivney1, Rasim Boyacıoğlu2, Stephen Jordan3, Ignacio Rozado4, Sherry Huang1, Siyuan Hu1, Brad Lackey3, Matthias Troyer3, Mark Griswold2, and Dan Ma1
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, Case Western Reserve University, Cleveland, OH, United States, 3Microsoft, Redmond, WA, United States, 41QB Information Technologies, Vancouver, BC, Canada

Iterative optimization in MRI is a large problem with many degrees of freedom. Depending on the cost function and parameters of interest, it may be beneficial to model errors from undersampling with non-Cartesian trajectories. This typically requires repeated use of the nonuniform FFT (NUFFT), which is computationally expensive. Here we propose an approximation based on a limited number of tissue types that eliminates the need for repeated NUFFTs, and allows a wide range of applications for sequence optimization in MRI and MRF.

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