[1]全科润,程品晶,陈榕钦,等.基于循环生成对抗网络的鼻咽癌CBCT图像修正[J].中国医学物理学杂志,2021,38(5):582-586.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.011]
 QUAN Kerun,CHENG Pinjing,CHEN Rongqin,et al.CBCT image correction for nasopharyngeal carcinoma based on cycle-consistent generative adversarial network[J].Chinese Journal of Medical Physics,2021,38(5):582-586.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.011]
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基于循环生成对抗网络的鼻咽癌CBCT图像修正()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第5期
页码:
582-586
栏目:
医学影像物理
出版日期:
2021-05-01

文章信息/Info

Title:
CBCT image correction for nasopharyngeal carcinoma based on cycle-consistent generative adversarial network
文章编号:
1005-202X(2021)05-0582-05
作者:
全科润1程品晶1陈榕钦2柏朋刚2陈济鸿2黄妙云3陈彦宇1洪加标2
1.南华大学核科学技术学院, 湖南 衡阳 421001; 2.福建省肿瘤医院放疗科, 福建 福州350014; 3.福建医科大学附属协和医院放疗科, 福建 福州 350001
Author(s):
QUAN Kerun1 CHENG Pinjing1 CHEN Rongqin2 BAI Penggang2 CHEN Jihong2 HUANG Miaoyun3 CHEN Yanyu1 HONG Jiabiao2
1. School of Nuclear Science and Technology, University of South China, Hengyang 421001, China 2. Department of Radiation Oncology, Fujian Provincial Cancer Hospital, Fuzhou 350014, China 3. Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, China
关键词:
鼻咽癌锥形束CT循环生成对抗网络
Keywords:
Keywords: nasopharyngeal carcinoma cone-beam computed tomography cycle-consistent generative adversarial network
分类号:
R318;R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2021.05.011
文献标志码:
A
摘要:
目的:利用循环生成对抗网络模型(CycleGAN)进行锥形束CT (CBCT)图像迁移,生成伪CT(sCT)图像,从而实现CBCT图像的HU值矫正。方法:回顾性分析在福建省肿瘤医院行放射治疗的鼻咽癌患者39例,所有患者均接受临床CT与CBCT扫描。以CBCT图像为基准,采用刚性配准算法对临床CT和CBCT进行配准,获得重采样计划CT(pCT)。经阈值分割及形态学处理获取配对影像的外轮廓内部区域作为掩膜,对配对影像进行掩膜操作及归一化预处理。建立CycleGAN神经网络,训练sCT生成模型。基于体素点计算平均绝对误差(MAE)和平均误差(ME),用于比较测试集sCT与pCT之间的差异。结果:测试集的sCT图像与pCT图像相比较,在体外轮廓内的MAE和ME分别为(99.00±15.37) HU和(-24.00±12.64) HU;软组织区域的MAE和ME分别为(48.00±7.45) HU和(-7.00±8.96) HU。结论:CycleGAN能修正CBCT图像的HU值,迁移生成的sCT图像具有与pCT图像近似的HU值及平滑性,可用于放射治疗剂量计算。
Abstract:
Abstract: Objective To realize HU correction for cone-beam computed tomography (CBCT) image by translating CBCT image into synthesized CT (sCT) via cycle-consistent generative adversarial network (CycleGAN). Methods Thirty-nine patients with nasopharyngeal carcinoma who underwent radiotherapy in Fujian Provincial Cancer Hospital were enrolled in the study and received clinical CT and CBCT scans. Taking CBCT image as benchmark, rigid registration algorithm was used for registration between clinical CT and CBCT, thereby obtaining resampling planning CT (pCT). Threshold segmentation and morphological processing were used to obtain the body region of the paired image as a mask and moreover, masking and normalizing pre-processing were carried out on the paired images. CycleGAN was established and then used to generate sCT images. Mean absolute error and mean error which were calculated based on voxels were adopted for comparing the differences between sCT and pCT. Results The comparison between sCT images and pCT images of test set showed that mean absolute error and mean error were (99.00±15.37) HU and (-24.00±12.64) HU for the whole body, and (48.00±7.45) HU and (-7.00±8.96) HU for soft tissues. Conclusion CycleGAN can be used for the HU correction for CBCT image, and the generated sCT image which has similar HU value and smoothness with pCT image can be used for dose calculation in radiotherapy.

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备注/Memo

备注/Memo:
【收稿日期】2021-01-05 【基金项目】福建省自然科学基金(2019Y0020,2015Y0010);福建省卫生健康科技项目(2017-ZQN-15,2018-ZQN-19);福建省科技计划项目(2018Y2003) 【作者简介】全科润,硕士研究生,研究方向:放射物理治疗,E-mail: 10- 61579562@qq.com 【通信作者】程品晶,博士,副教授,研究方向:核技术应用,E-mail: nhuchpj@aliyun.com;柏朋刚,高级工程师,研究方向:医学图像处理,E-mail: littwind@sina.cn
更新日期/Last Update: 2021-05-31