Keywords: Arterial spin labelling, Arterial spin labellingA simulation-based supervised neural network was developed for simple and robust parameter estimation from multi-delay DANTE-prepared arterial spin labeling (ASL). The network was trained using 15 million simulation data points. Accuracy and precision were compared between the proposed and conventional methods. The neural-network-based estimation presented higher accuracy and precision than the conventional method that used table lookup. A higher noise immunity was also observed with the proposed method. A simulation-based supervised neural network simplifies the estimation process of multiparametric ASL. The estimation performance of cerebral blood flow and arterial cerebral blood volume was particularly improved by the proposed method.
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