Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy
Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy
复制标题
使用CycleGAN从锥形束计算机断层扫描（CBCT）生成合成计算机断层扫描（CT）以用于自适应放射治疗
DOI：
10.1088/1361-6560/ab22f9
复制
发表时间：
2019-06-01
影响因子：
3.5
通讯作者：
Jiang, Steve
中科院分区：
文献类型：
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作者：
Liang, Xiao;Chen, Liyuan;Jiang, Steve
在提供放射治疗的整个过程中，可能需要几个星期的时间，患者的解剖结构可能会发生巨大变化，可能需要适应性放射治疗(ART)。锥束计算机断层扫描(CBCT)通常在治疗过程中使用，可用于患者定位和ART重新规划。然而，由于存在明显的噪声、伪影和不准确的Hounsfield单位(HU)值，基于CBCT图像的剂量计算可能不能准确地用于治疗计划。解决这一问题的一种方法是将CBCT图像转换为更精确的合成CT(SCT)图像。在这项工作中，我们开发了一个周期一致的生成性对抗网络框架(CycleGAN)来从CBCT图像合成CT图像。该模型能够在无监督学习环境下使用未配对的CT和CBCT图像进行图像到图像的转换。通过该模型从CBCT生成的SCT图像在视觉和定量上与实际CT图像相似，对于头颈部(H&N)癌症患者，平均绝对误差(MAE)从69.29HU降至29.85HU。在三维伽马指数分析中，以变形计划CT图像(DpCT)为参照物，在1 mm/1%标准下，伽马指数通过率由86.92%提高到96.26%，CycleGAN在SCT上计算的剂量分布比CBCT更准确。我们还将CycleGAN模型与其他非监督学习方法进行了比较，其中包括深度卷积生成对抗网络(DCGAN)和渐进式Gans生长(PGGAN)，结果表明CycleGAN模型的性能优于其他两种模型。体模研究比较了SCT和DpCT，结构相似指数从0.91增加到0.93表明CycleGAN在保持解剖准确性方面比DIR表现得更好。
Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.