南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (1): 179-186.doi: 10.12122/j.issn.1673-4254.2025.01.21

• • 上一篇    

基于PE-CycleGAN网络的鼻咽癌自适应放疗CBCT-sCT生成研究

贺亚迪1,2(), 周炫汝1, 金锦辉1, 宋婷1()   

  1. 1.南方医科大学生物医学工程学院,广东 广州 510515
    2.南方医科大学中西医结合医院放疗科,广东 广州 510315
  • 收稿日期:2024-07-15 出版日期:2025-01-20 发布日期:2025-01-20
  • 通讯作者: 宋婷 E-mail:540651179@qq.com;tingsong2015@smu.edu.cn
  • 作者简介:贺亚迪,在读硕士研究生,E-mail: 540651179@qq.com
  • 基金资助:
    国家自然科学基金(82472117);广东省基础与应用基础研究基金面上项目(2024A1515011831)

PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy

Yadi HE1,2(), Xuanru ZHOU1, Jinhui JIN1, Ting SONG1()   

  1. 1.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
    2.Department of Radiotherapy, Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou 510315, China
  • Received:2024-07-15 Online:2025-01-20 Published:2025-01-20
  • Contact: Ting SONG E-mail:540651179@qq.com;tingsong2015@smu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(82472117)

摘要:

目的 探索基于PE-CycleGAN方法从锥形束CT(CBCT)合成高质量CT(sCT),用于鼻咽癌自适应放疗(ART)。 方法 提出感知增强的CycleGAN模型(PE-CycleGAN),引入判别器双重对比度损失、生成器多感知损失和改进的U-Net结构。采用80例鼻咽癌患者的CBCT和CT作为训练集,7例为测试集。通过量化sCT与参考CT的平均绝对误差(MAE)、峰值信噪比(PSNR)、结构相似性指标(SSIM)以及sCT与参考CT剂量gamma通过率、靶区和危及器官(OAR)相对剂量偏差,评估sCT图像质量和剂量计算精度。 结果 PE-CycleGAN生成sCT与对应标准CT的MAE为56.89±13.84 HU,较CBCT的81.06±15.86 HU降低约30%(P<0.001)。PE-CycleGAN的PSNR和SSIM(26.69±2.41 dB,0.92±0.02)高于CBCT(21.54±2.37 dB,0.86±0.05)(P<0.001)。在与计划CT剂量gamma分析中,在2 mm/2%标准下,PE-CycleGAN的sCT剂量比对通过率(90.13±3.75)%高于CBCT的(81.65±3.92)% (P<0.001)和CycleGAN的(87.69±3.50)% (P<0.05)。在3 mm/3%标准下,PE-CycleGAN 的sCT通过率(97.20±2.52)%同样优于CBCT的(86.92±3.51)% (P<0.001)和CycleGAN的(94.58±2.23)% (P<0.01)。sCT相比计划CT的靶区和OAR相对剂量偏差均值除Lens Dmax(Gy)为3.38%(P=0.09)外都在±3%范围内(P>0.05),PTVnx HI、PTVnd HI、PTVnd CI、PTV1 HI、PRV_SC、PRV_BS、Parotid、Larynx、Oral、Mandible、PRV_ON相对剂量偏差均值都小于±1%(P>0.05)。 结论 PE-CycleGAN能从CBCT快速合成高质量sCT,可用于鼻咽癌的CBCT引导ART。

关键词: 锥形束CT, 合成CT, 自适应放疗, 深度学习, 生成对抗网络

Abstract:

Objective To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. Methods A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. Results The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). Conclusion PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.

Key words: cone-beam CT, synthetic CT images, adaptive radiotherapy, deep learning, generative adversarial network