To overcome the difficulty of obtaining a large number of real training samples, the utilization of synthetic training samples based on Bloch simulation has become more and more popular in deep learning based MRI reconstruction. However, a large amount of Bloch simulation is usually very time-consuming even with the help of GPU. In this study, a simulation network that receives sequence parameters and contrast templates, was proposed to simulate MR images from different imaging sequences. The reliability and flexibility of the proposed method were verified by distortion correction for GRE-EPI images and T2 maps obtained with overlapping-echo detachment planar imaging.
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