An approach to fMRI image reconstruction for variable density radial trajectories is proposed in this abstract. We have employed Generative Adversarial Networks (GAN), which is made up of a generator and a discriminator, to map input aliasing images to gold standard images. Different from the large computation requirements of CS-based methods, the proposed method is able to both boost reconstruction efficiency and achieve a good image quality in the meantime.
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