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Abstract #2783

Deep Generative Adversarial Networks for High Resolution fMRI using Variable Density Spiral Sampling

Tianle Cao1, Xuesong Li1, Yan Tong2, and Hua Guo1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2University of Oxford, London, United Kingdom

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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