Computed tomography (CT) is critical for various clinical applications, e.g.,
radiotherapy treatment planning and also PET attenuation correction. However,
CT exposes radiation during acquisition, which may cause side effects to
patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and
does not involve any radiations. Therefore, recently, researchers are greatly
motivated to estimate CT image from its corresponding MR image of the same
subject for the case of radiotherapy planning. In this paper, we propose a
data-driven approach to address this challenging problem. Specifically, we
train a fully convolutional network to generate CT given an MR image. To better
model the nonlinear relationship from MRI to CT and to produce more realistic
images, we propose to use the adversarial training strategy and an image
gradient difference loss function. We further apply AutoContext Model to
implement a context-aware generative adversarial network. Experimental results
show that our method is accurate and robust for predicting CT images from MRI
images, and also outperforms three state-of-the-art methods under comparison.

本文提出了一种数据驱动的全卷积神经网络，利用对抗训练和图像梯度差异损失函数来更好地建模 MRI 到 CT 的非线性关系，进而生成与之对应的 CT 图像，实验证明其准确性和稳健性，并且表现优于已有的三种方法。