Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy
Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy
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DOI：
10.1016/j.ejmp.2020.11.007
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发表时间：
2020-12-01
影响因子：
3.4
通讯作者：
Fleckenstein, Jens
中科院分区：
文献类型：
--
作者：
Eckl, Miriam;Hoppen, Lea;Fleckenstein, Jens
目的：图像引导放射治疗可以从实施适应性放射治疗（ART）技术中获益。对基于循环生成对抗网络（cycle-GAN）的锥束计算机断层扫描（CBCT）到合成CT （sCT）转换算法进行了图像质量、图像分割和头颈部（H&N）、胸部和骨盆身体区域的剂量学精度评估。方法：使用循环gan，预先使用kV成像系统的独立配对CT和CBCT数据集训练三个身体部位特异性模型（XVI, Elekta）。每个身体区域的15例患者基于一阶CBCT生成sCT。分析sCT的平均误差（ME）和平均绝对误差（MAE）。在sCT上，将人工描绘的结构与规划CT （pCT）的变形结构进行比较，并使用标准分割指标进行评估。在sCT上重新计算治疗方案。比较临床相关剂量-体积参数（靶体积的D-98、D-50和D-2）和3d - γ (3%/3mm)分析。结果：H&N、胸椎、盆腔ME、MAE分别为1.4、29.6、5.4 Hounsfield单位（HU）和77.2、94.2、41.8单位（HU）。精囊的相似系数为66.7 +/- 8.3%，肺的相似系数为94.9 +/- 2.0%。最大平均表面距离为6.3 mm（心脏），其次是3.5 mm（脑干）。靶体积的平均剂量学差异不超过1.7%。所有病例的平均3D伽马通过率均大于97.8%。结论：本方法生成的sCT图像质量接近pCT，并产生临床可接受的剂量学偏差。因此，满足了临床实施基于cbctart的重要先决条件。
Purpose: Image-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.Methods: Using a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D-98, D-50 and D-2 of the target volume) and 3D-gamma (3%/3mm) analysis were performed.Results: The mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 +/- 8.3% (seminal vesicles) and 94.9 +/- 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.Conclusions: The presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCTbased ART is fulfilled.