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.
This abstract and the presentation materials are available to members only; a login is required.