兆伏级CT图像引导自适应放疗中生成合成CT研究  

Generating synthetic CT in megavoltage CT image-guided adaptive radiotherapy

作　　者：陈宇亭 周飞宇 张富利 蒋华勇 陈点点[2] 高彦祥 郁艳军 乐小云[1] 路娜[2] CHEN Yuting;ZHOU Feiyu;ZHANG Fui;JIANG Huayong;CHEN Diandian;GAO Yanxiang;YU Yanjun;LE Xiaoyun;LU Na(School of Physics,Beihang University,Beijing 100191,China;Department of Radiotherapy,the 7th Medical Center,Department of Oncology,Chinese PLA General Hospital,Beijing 100700,China)

机构地区：[1]北京航空航天大学物理学院,北京100191 [2]解放军总医院肿瘤学部第七医学中心放疗科,北京100700

出　　处：《中国医学物理学杂志》2024年第7期813-820,共8页Chinese Journal of Medical Physics

基　　金：解放军总医院第七医学中心创新培育基金(qzx-2023-12)。

摘　　要：目的:开发一种基于深度学习神经网络的方法将宫颈癌MVCT图像转换为具有高信噪比和高对比度的伪kVCT图像,从而提供宫颈癌自适应放疗需要的患者三维解剖图像和定位信息,引导加速器实现精确放疗。方法:收集54例宫颈癌患者的MVCT和kVCT图像组成数据集,随机选择44例样本作为训练集,并将剩下的10例样本作为测试集。采用加入门控机制和多通道数据输入的循环生成对抗网络(CycleGAN)基于MVCT合成伪kVCT图像。采用平均绝对误差(MAE)、峰值信噪比(PSNR)和结构相似度指数(SSIM)等影像学成像质量评估参数,评估网络训练效果。结果:5通道MVCT-5通道kVCT图像与MVCT图像对比,MAE从(24.9±0.7)HU降至(17.8±0.3)HU,PSNR从(29.8±0.2)dB升至(30.7±0.2)dB,SSIM从0.841±0.007升至0.898±0.003。结论:该方法生成的伪kVCT在降噪和增强对比度方面具有优势,同时能够减少剂量计算中对额外MV-kVCT电子密度校准的需求。伪kVCT的剂量计算能力与MVCT相当,为伪kVCT影像应用于图像引导自适应放疗提供了可能。Objective To propose a deep learning neural network approach for transforming megavoltage computed tomography(MVCT)images of cervical cancer into pseudo kilovoltage computed tomography(kVCT)images with high signal-to-noise ratio and contrast-to-noise ratio,thus providing three-dimensional anatomical images and localization information required for adaptive radiotherapy of cervical cancer,and guiding the accelerator to achieve precise treatment.Methods The MVCT and kVCT images of 54 patients treated with cervical cancer radiotherapy were collected,with 44 cases randomly selected as the training set,and the remaining 10 cases as the test set.A cyclic generative adversarial network with gating mechanism and multi-channel data input was used to synthesize pseudo-kVCT images from MVCT images.The network training results were evaluated with imaging quality evaluation parameters,such as mean absolute error(MAE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM).Results The MAE,PSNR,and SSIM of MVCT images vs pseudo-kVCT(5:5)images were(24.9±0.7)HU vs(17.8±0.3)HU,(29.8±0.2)dB vs(30.7±0.2)dB,and 0.841±0.007 vs 0.898±0.003,respectively.Conclusion The generated pseudo-kVCT images have advantages in noise reduction and contrast enhancement,and can reduce the need for additional MV-kVCT electron density calibration in dose calculations.The dose calculation ability of pseudo-kVCT is comparable to that of MVCT,providing a possibility for the application of pseudo-kVCT images in image-guided adaptive radiotherapy.

关 键 词：循环生成对抗网络 MVCT 合成CT 图像引导放疗 图像质量 

分 类 号：R737.33[医药卫生—肿瘤]