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Tytuł:
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New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer.
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Autorzy:
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Xie K; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Gao L; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Xi Q; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
Zhang H; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
Zhang S; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
Zhang F; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
Sun J; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Lin T; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Sui J; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China.
Ni X; Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China. Electronic address: .
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Źródło:
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Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2023 Apr; Vol. 231, pp. 107393. Date of Electronic Publication: 2023 Feb 01.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
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MeSH Terms:
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Spiral Cone-Beam Computed Tomography*
Breast Neoplasms*
Humans ; Female ; Radiotherapy Planning, Computer-Assisted/methods ; Cone-Beam Computed Tomography/methods ; Radiometry
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Contributed Indexing:
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Keywords: Breast cancer radiotherapy; Synthesized CT; Truncated low-dose CBCT; Unsupervised deep learning
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Entry Date(s):
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Date Created: 20230205 Date Completed: 20230314 Latest Revision: 20230314
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Update Code:
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20240105
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DOI:
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10.1016/j.cmpb.2023.107393
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PMID:
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36739623
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Objective: A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer.
Methods: The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN).
Results: The three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet.
Conclusions: High-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.
Competing Interests: Declaration of Competing Interest None.
(Copyright © 2023. Published by Elsevier B.V.)
