论文标题
注意引导的生成对抗网络，以解决模态转移的非典型解剖
Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer
论文作者
论文摘要
最近，使用合成CTS（Syntcts）对仅MR治疗计划的兴趣在放射治疗中迅速增长。但是，开发包含非典型解剖结构的医学图像的类解决方案仍然是一个主要限制。在本文中，我们提出了一种新型的空间注意力引导的生成对抗网络（注意力机）模型，以使用T1加权MRI图像作为解决非典型解剖结构的输入来生成准确的突触。对十五名脑癌患者的实验结果表明，注意力很高的人的表现优于现有的Syntct模型，并获得了85.22 $ \ pm的平均MAE $ 12.08，232.41 $ \ pm $ \ pm $ 60.86，246.38 $ \ pm $ 42.67 hounsfield untist and hounsfield untist and hounsfield untits and syncte and ct-sim在整个头部，骨骼和空中区域之间，相应地相应。定性分析表明，注意力机具有使用空间集中区域来更好地处理异常值，具有复杂解剖结构或后手术后区域的区域的能力，因此为支持接近实时的MR唯一治疗计划提供了强大的潜力。
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that contain atypical anatomy remains a major limitation. In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images as the input to address atypical anatomy. Experimental results on fifteen brain cancer patients show that attention-GAN outperformed existing synCT models and achieved an average MAE of 85.22$\pm$12.08, 232.41$\pm$60.86, 246.38$\pm$42.67 Hounsfield units between synCT and CT-SIM across the entire head, bone and air regions, respectively. Qualitative analysis shows that attention-GAN has the ability to use spatially focused areas to better handle outliers, areas with complex anatomy or post-surgical regions, and thus offer strong potential for supporting near real-time MR-only treatment planning.
