Unsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprinting

The optimal design of the Magnetic resonance fingerprinting (MRF) sequence is still challenging due to the optimization of high-degrees-of-freedom acquisition parameters. In this paper, we propose a novel unsupervised learning-based pulse sequence design framework for efficient MRF sequence optimization. Specifically, we propose a novel pulse sequence generation network (PSG-Net) that fully exploits the sequence correlation to generate the optimal pulse sequence from a zero-initialized input. To achieve improved precision of parameter estimation, we use a predefined pulse sequence performance evaluation function that can directly represent tissue quantification separability as the loss function to update the parameters of the PSG-Net.