Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.

Journal: Technology in cancer research & treatment
PMID: 34851204

Abstract

To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Authors

  • Xudong Xue
    117922Hubei Cancer Hospital, Tongji Medical College, Huzhong University of Science and Technology, Wuhan, Hubei, China.
  • Yi Ding
    Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Jun Shi
    School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: junshi@staff.shu.edu.cn.
  • Xiaoyu Hao
    546497School of Computer Science and Technology, 12652University of Science and Technology of China, Hefei, 117556Anhui, China.
  • Xiangbin Li
    Stonewise, No. 19 Zhongguancun Street, Haidian District, 100080 Beijing, P. R. China.
  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.
  • Yuan Wu
    State Key Laboratory of Precision Spectroscopy, Quantum Institute for Light and Atoms, Department of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
  • Hong An
    546497School of Computer Science and Technology, 12652University of Science and Technology of China, Hefei, 117556Anhui, China.
  • Man Jiang
    School of Energy and Power Engineering, 12443Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Xiao Wang
    Research Centre of Basic Integrative Medicine, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.

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