Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.

Document Type

Article

Publication Date

2-22-2023

Publication Title

Frontiers in Oncology

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.

Volume

13

First Page

1127866

Last Page

1127866

DOI

10.3389/fonc.2023.1127866

ISSN

2234-943X

PubMed ID

36910636

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