Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy
PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with resid...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.603844/full |
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| author | Hongfei Sun Rongbo Fan Chunying Li Chunying Li Chunying Li Zhengda Lu Zhengda Lu Zhengda Lu Kai Xie Kai Xie Kai Xie Xinye Ni Xinye Ni Xinye Ni Jianhua Yang |
| author_facet | Hongfei Sun Rongbo Fan Chunying Li Chunying Li Chunying Li Zhengda Lu Zhengda Lu Zhengda Lu Kai Xie Kai Xie Kai Xie Xinye Ni Xinye Ni Xinye Ni Jianhua Yang |
| author_sort | Hongfei Sun |
| collection | DOAJ |
| description | PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure. |
| first_indexed | 2024-12-20T00:01:52Z |
| format | Article |
| id | doaj.art-e9a00a6b9f634ff4a7311ea7dcf72eda |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-20T00:01:52Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-e9a00a6b9f634ff4a7311ea7dcf72eda2022-12-21T20:00:49ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-03-011110.3389/fonc.2021.603844603844Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in RadiotherapyHongfei Sun0Rongbo Fan1Chunying Li2Chunying Li3Chunying Li4Zhengda Lu5Zhengda Lu6Zhengda Lu7Kai Xie8Kai Xie9Kai Xie10Xinye Ni11Xinye Ni12Xinye Ni13Jianhua Yang14School of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaDepartment of Radiotherapy, Second People’s Hospital of Changzhou, Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Center of Medical Physics With Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Key Laboratory of Medical Physics With Changzhou, Changzhou, ChinaDepartment of Radiotherapy, Second People’s Hospital of Changzhou, Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Center of Medical Physics With Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Key Laboratory of Medical Physics With Changzhou, Changzhou, ChinaDepartment of Radiotherapy, Second People’s Hospital of Changzhou, Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Center of Medical Physics With Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Key Laboratory of Medical Physics With Changzhou, Changzhou, ChinaDepartment of Radiotherapy, Second People’s Hospital of Changzhou, Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Center of Medical Physics With Nanjing Medical University, Changzhou, ChinaDepartment of Radiotherapy, The Key Laboratory of Medical Physics With Changzhou, Changzhou, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaPurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.https://www.frontiersin.org/articles/10.3389/fonc.2021.603844/fullpseudo computed tomography (CT)CycleGANcone-beam computed tomography (CT)radiotherapycervical cancer |
| spellingShingle | Hongfei Sun Rongbo Fan Chunying Li Chunying Li Chunying Li Zhengda Lu Zhengda Lu Zhengda Lu Kai Xie Kai Xie Kai Xie Xinye Ni Xinye Ni Xinye Ni Jianhua Yang Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy Frontiers in Oncology pseudo computed tomography (CT) CycleGAN cone-beam computed tomography (CT) radiotherapy cervical cancer |
| title | Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy |
| title_full | Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy |
| title_fullStr | Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy |
| title_full_unstemmed | Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy |
| title_short | Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy |
| title_sort | imaging study of pseudo ct synthesized from cone beam ct based on 3d cyclegan in radiotherapy |
| topic | pseudo computed tomography (CT) CycleGAN cone-beam computed tomography (CT) radiotherapy cervical cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.603844/full |
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