Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning
Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning
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DOI：
10.1016/j.ejmp.2020.08.018
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发表时间：
2020-10-01
影响因子：
3.4
通讯作者：
Tanigawa, Noboru
中科院分区：
文献类型：
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作者：
Koike, Yuhei;Anetai, Yusuke;Tanigawa, Noboru
目的：使用非配对数据开发基于深度学习的金属伪影减少（DL-MAR）方法，并与水密度覆盖方法（water density override method）相比，评估其在头颈部调强放射治疗（IMRT）中的剂量学影响。15名患者的牙齿填充物被用作测试数据集。其余92例患者的计算机断层扫描（CT）图像分为两个区域：金属伪影和无伪影区域。Cyc 1 eGAN用于结构域翻译。将DL-MAR图像的伪影指数与原始未校正（UC）CT图像的伪影指数进行比较。通过比较每个数据集中的参考临床计划与水密度覆盖方法（水计划），创建DL-MAR和UC计划的剂量分布。结果：在所有患者中，DL-MAR图像的伪影指数显著小于UC图像的伪影指数（13.2 +/- 4.3 vs. 267.3 +/- 113.7）。与参考水计划相比，UC计划的剂量差异大于DL-MAR计划。DL-MAR图像提供的剂量测定结果是更相似的水计划比UC planne.Conclusions：我们开发了一种快速DL-MAR方法使用Cyc 1 eGAN头颈部调强放射治疗。所提出的方法可以提供一致的剂量计算对金属伪影和提高规划过程的效率，通过消除手动划定。
Purpose: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method.Methods: The data set comprised the data of 107 patients who underwent radiotherapy. Fifteen patients with dental fillings were used as the test data set. The computed tomography (CT) images of the remaining 92 patients were divided into two domains: the metal artifact and artifact-free domains. Cyc1eGAN was used for domain translation. The artifact index of the DL-MAR images was compared with that of the original uncorrected (UC) CT images. The dose distributions of the DL-MAR and UC plans were created by comparing the reference clinical plan with the water density override method (water plan) in each dataset. Dosimetric deviation in the oral cavity from the water plan was evaluated.Results: The artifact index of the DL-MAR images was significantly smaller than that of the UC images in all patients (13.2 +/- 4.3 vs. 267.3 +/- 113.7). Compared with the reference water plan, dose differences of the UC plans were greater than those of the DL-MAR plans. DL-MAR images provided dosimetric results that were more similar to those of the water plan than the UC plan.Conclusions: We developed a fast DL-MAR method using Cyc1eGAN for head and neck IMRT. The proposed method could provide consistent dose calculation against metal artifact and improve the efficiency of the planning process by eliminating manual delineation.