Parallel imaging with a combination of sensitivity encoding and generative adversarial networks

参赛者：

吕骏-烟台大学 （2018年博士毕业于北京大学）

作品简介：

我们提出了一种加速MRI重建的新方法，该方法结合了SENSE重建和GAN网络。结果表明，该方法能够从高倍欠采样的K空间成功恢复丢失的图像信息，并且能够有效消除SENSE重建中明显的g-因子伪影，有希望应用于临床。

We have presented a novel framework for accelerated MRI reconstruction by combining SENSE reconstruction with GAN. Results show that the proposed method is capable of producing faithful image reconstructions from highly under-sampled k-space data and removing the g-factor artifacts effectively, which is promising for many clinical applications.