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Abstract #0169

Sequence Design for Fast and Robust MR Fingerprinting Scans using Quantum Optimization

Siyuan Hu1, Ignacio Rozada2, Rasim Boyacioglu3, Stephen Jordan4, Sherry Huang3, Matthias Troyer4, Mark Griswold3, Debra McGivney3, and Dan Ma3
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 21Qbit, Vancouver, BC, Canada, 3Case Western Reserve University, Cleveland, OH, United States, 4Microsoft, Redmond, WA, United States

MR Fingerprinting is able to quantify multiple tissue properties simultaneously. Here we propose an advanced MR Fingerprinting optimization framework that computes and minimizes the quantitative random errors, undersampling errors and background phase errors in MRF maps simultaneously in the cost function. The optimization is solved by quantum-inspired algorithms. The proposed framework could provide accelerated MRF scans that are robust to undersampling and system imperfections, and outperform the human-designed sequence on the tradeoff between duration and precision.

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