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Abstract #3744

Quantum Optimization Framework for MR Fingerprinting Framework Incorporating Undersampling and Noise

Siyuan Hu1, Ignacio Rozada2, Rasim Boyacioglu3, Stephen Jordan4, Sherry Huang1, Matthias Troyer4, Mark Griswold3, Debra McGivney1, and Dan Ma1
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 21Qbit, Vancouver, BC, Canada, 3Radiology, Case Western Reserve University, Cleveland, OH, United States, 4Microsoft, Redmond, WA, United States

MR fingerprinting is a novel quantitative MR imaging technique that provides multiple tissue properties maps simultaneously. Designing appropriate MR fingerprinting sequence patterns is crucial to speed up data acquisition while obtaining accurate measurements. Here we propose an advanced MR fingerprinting optimization framework that incorporates undersampling artifacts and random noise in the cost function which directly compute quantitative errors in the result maps. We use quantum-inspired algorithm to solve the problem and generate optimized sequences. In both simulation and in vivo experiments, the optimized sequence showed improved image quality and measurement accuracy.

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