The purpose of this study is to propose a fast T1 estimation method with improved accuracy over existing approaches in a Multiple Flip Angle setting. A supervised machine learning based approach has been proposed that can be used to predict additional Flip Angle data using limited available Flip Angle data, thereby producing more accurate T1 estimation in reduced scan time. Both experimental as well as simulation results are shown to illustrate the efficacy of this approach. The accuracy of T1 estimation depends on the choice of Flip Angle data to be predicted.
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