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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