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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