Technical note: MR image-based synthesis CT for CyberKnife robotic stereotactic radiosurgery.
Biomed Phys Eng Express
; 10(5)2024 Aug 13.
Article
em En
| MEDLINE
| ID: mdl-39094608
ABSTRACT
The purpose of this study is to investigate whether deep learning-based sCT images enable accurate dose calculation in CK robotic stereotactic radiosurgery. A U-net convolutional neural network was trained using 2446 MR-CT pairs and used it to translate 551 MR images to sCT images for testing. The sCT of CK patient was encapsulated into a quality assurance (QA) validation phantom for dose verification. The CT value difference between CT and sCT was evaluated using mean absolute error (MAE) and the statistical significance of dose differences between CT and sCT was tested using the Wilcoxon signed rank test. For all CK patients, the MAE value of the whole brain region did not exceed 25 HU. The percentage dose difference between CT and sCT was less than ±0.4% on GTV (D2(Gy), -0.29%, D95(Gy), -0.09%), PTV (D2(Gy), -0.25%, D95(Gy), -0.10%), and brainstem (max dose(Gy), 0.31%). The percentage dose difference between CT and sCT for most regions of interest (ROIs) was no more than ±0.04%. This study extended MR-based sCT prediction to CK robotic stereotactic radiosurgery, expanding the application scenarios of MR-only radiation therapy. The results demonstrated the remarkable accuracy of dose calculation on sCT for patients treated with CK robotic stereotactic radiosurgery.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Dosagem Radioterapêutica
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Planejamento da Radioterapia Assistida por Computador
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Imageamento por Ressonância Magnética
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Tomografia Computadorizada por Raios X
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Radiocirurgia
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Imagens de Fantasmas
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Procedimentos Cirúrgicos Robóticos
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Biomed Phys Eng Express
Ano de publicação:
2024
Tipo de documento:
Article