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Abstract #4524

Optimization of MR Fingerprinting Sequence Using a Quantum Inspired Algorithm

Dan Ma1, Stephen Jordan2, Rasim Boyacioglu1, Michael Beverland2, Yun Jiang1, Darryl Jacob3, Sherry Huang4, Helmut G Katzgraber2, Julie Love2, Mark A Griswold5, Matthias Troyer2, and Debra F McGivney1

1Radiology, Case Western Reserve University, School of Medicine, Cleveland, OH, United States, 2Microsoft, Seattle, WA, United States, 3Physics and Astronomy, Texas A&M University, College Station, TX, United States, 4Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 5Case Western Reserve University, School of Medicine, Cleveland, OH, United States

MR Fingerprinting (MRF) is a fast quantitative MR imaging technique that simultaneously quantifies multiple tissue properties. We propose to use quantum-inspired optimization to characterize the optimization landscape by using an appropriate cost function to account for signal features and create an optimization frontier. The simulation results from the optimized MRF sequences showed reduced bias and variance as compared to those from the original empirical design. The in vivo maps from the optimized sequences showed improved image quality as well.

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