Medical Image Synthesis with Context-Aware Generative Adversarial Networks.
Medical Image Synthesis with Context-Aware Generative Adversarial Networks.
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DOI：
10.1007/978-3-319-66179-7_48
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发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Nie D;Trullo R;Lian J;Petitjean C;Ruan S;Wang Q;Shen D
计算机断层扫描（CT）对于各种临床应用是至关重要的，例如，放射治疗计划以及MRI/PET扫描仪中的PET衰减校正。然而，CT在采集期间暴露辐射，这可能对患者造成副作用。与CT相比，磁共振成像（MRI）更安全，不涉及辐射。因此，近年来研究人员极大的动机，估计CT图像从其对应的MR图像的同一主题的情况下，辐射计划。在本文中，我们提出了一种数据驱动的方法来解决这个具有挑战性的问题。具体来说，我们训练一个全卷积网络（FCN）来生成给定MR图像的CT。为了更好地模拟从MRI到CT的非线性映射并生成更逼真的图像，我们建议使用对抗训练策略来训练FCN。此外，我们提出了一个基于图像梯度差的损失函数，以减轻所产生的CT的模糊。我们进一步应用自动上下文模型（ACM）来实现一个上下文感知的生成对抗网络。实验结果表明，我们的方法是准确和鲁棒的预测CT图像从MR图像，也优于三个国家的最先进的方法进行比较。
Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. 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 radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.