Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy
Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 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 we...
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SAGE Publishing
2021
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oai:doaj.org-article:7da452e2c3014ae69531093403bf99612021-12-02T08:03:20ZCone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy1533-033810.1177/15330338211062415https://doaj.org/article/7da452e2c3014ae69531093403bf99612021-12-01T00:00:00Zhttps://doi.org/10.1177/15330338211062415https://doaj.org/toc/1533-0338Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 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. Results: 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%. Conclusions: 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.Xudong Xue PhDYi Ding PhDJun Shi MSXiaoyu Hao MSXiangbin Li PhDDan Li MSYuan Wu PhDHong An PhDMan Jiang PhDWei Wei PhDXiao Wang PhDSAGE PublishingarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENTechnology in Cancer Research & Treatment, Vol 20 (2021) |
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DOAJ |
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DOAJ |
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EN |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Xudong Xue PhD Yi Ding PhD Jun Shi MS Xiaoyu Hao MS Xiangbin Li PhD Dan Li MS Yuan Wu PhD Hong An PhD Man Jiang PhD Wei Wei PhD Xiao Wang PhD Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| description |
Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 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. Results: 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%. Conclusions: 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. |
| format |
article |
| author |
Xudong Xue PhD Yi Ding PhD Jun Shi MS Xiaoyu Hao MS Xiangbin Li PhD Dan Li MS Yuan Wu PhD Hong An PhD Man Jiang PhD Wei Wei PhD Xiao Wang PhD |
| author_facet |
Xudong Xue PhD Yi Ding PhD Jun Shi MS Xiaoyu Hao MS Xiangbin Li PhD Dan Li MS Yuan Wu PhD Hong An PhD Man Jiang PhD Wei Wei PhD Xiao Wang PhD |
| author_sort |
Xudong Xue PhD |
| title |
Cone Beam CT (CBCT) Based Synthetic
CT Generation Using Deep Learning
Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| title_short |
Cone Beam CT (CBCT) Based Synthetic
CT Generation Using Deep Learning
Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| title_full |
Cone Beam CT (CBCT) Based Synthetic
CT Generation Using Deep Learning
Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| title_fullStr |
Cone Beam CT (CBCT) Based Synthetic
CT Generation Using Deep Learning
Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| title_full_unstemmed |
Cone Beam CT (CBCT) Based Synthetic
CT Generation Using Deep Learning
Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy |
| title_sort |
cone beam ct (cbct) based synthetic
ct generation using deep learning
methods for dose calculation of nasopharyngeal carcinoma radiotherapy |
| publisher |
SAGE Publishing |
| publishDate |
2021 |
| url |
https://doaj.org/article/7da452e2c3014ae69531093403bf9961 |
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