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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