ArticleFrontiers in oncology2023
Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment.Frontiers in oncology · 2024Article
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9 authors.
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Abstract
Objective: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR). Methods: This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images Results: The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region. Conclusion: The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.
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