Fast and accurate modeling of transient-state sequences are required for various quantitative MR applications. We present here a surrogate model based on Recurrent Neural Network (RNN) architecture, to quickly compute large-scale MR signals and derivatives. We demonstrate that the trained RNN model works with different sequence parameters and tiussue parameters without the need of retraining. We prove that the RNN model can be used for computing large-scale MR signals and derivatives within seconds, and therefore achieves one to three orders of magnitude acceleration for different qMRI applications.
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