Computed tomography (CT) plays key roles in radiotherapy treatment planning and PET attenuation correction (AC). Magnetic resonance (MR) imaging has better soft tissue contrast than CT and has no ionizing radiation but cannot directly provide information about photon interactions with tissue that is needed for radiation treatment planning and AC. Therefore, estimating synthetic CT (sCT) images from corresponding MR images and obviating CT scanning is of great interest, but can be particularly challenging in the abdomen owing to a range of tissue types and physiologic motion. For this purpose, inspired by deep learning, we design a novel generative adversarial network (GAN) model that organically combines ResNet, U-net, and auxiliary classifier-augmented GAN (RU-ACGAN for short). The significance of our effort is three-fold: 1) The combination of ResNet and U-net, instead of only the U-net which was commonly used in existing conditional GAN, is enlisted to constitute the generative network in RU-ACGAN. This has the potential to generate more accurate CT than existing methods. 2) Adding the classifier to the discriminant network makes the training process of the proposed model more stable, and thereby benefits the robustness of sCT estimation. 3) Owing to the delicate architecture, RU-ACGAN is capable of estimating superior sCT using only a limited quantity of training data. The experimental studies on ten subjects’ MR-CT pair images indicate that the proposed RU-ACGAN model can capture the potential, non-linear matching between the MR and CT images, and thus achieves the better performance for sCT estimation for the abdomen than many other existing methods.

中文翻译：

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使用新型生成对抗网络从MR腹部图像估算CT

计算机断层扫描（CT）在放射治疗计划和PET衰减校正（AC）中起着关键作用。磁共振（MR）成像具有比CT更好的软组织对比度，并且没有电离辐射，但是不能直接提供放射线治疗计划和交流所需的有关光子与组织相互作用的信息。因此，从相应的MR图像估计合成CT（sCT）图像并避免CT扫描是非常令人感兴趣的方法，但是由于组织类型和生理运动范围的原因，在腹部尤其具有挑战性。为此，在深度学习的启发下，我们设计了一种新颖的生成对抗网络（GAN）模型，该模型将ResNet，U-net和辅助分类器增强GAN（简称RU-ACGAN）有机地结合在一起。我们努力的意义有三方面：1）要求ResNet和U-net的结合，而不是仅在现有的条件GAN中通常使用的U-net，才能构成RU-ACGAN中的生成网络。与现有方法相比，这有可能产生更精确的CT。2）将分类器添加到判别网络可以使所提出模型的训练过程更加稳定，从而有利于sCT估计的鲁棒性。3）由于其精致的架构，RU-ACGAN能够仅使用有限的训练数据来估计卓越的sCT。对十个受试者的MR-CT对图像进行的实验研究表明，所提出的RU-ACGAN模型可以捕获MR和CT图像之间潜在的非线性匹配，因此与许多人相比，在腹部sCT估计方面具有更好的性能。其他现有方法。