| Autor: |
Geert De Kerf, Michaël Claessens, Isabelle Mollaert, Wim Vingerhoed, Dirk Verellen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Technical Innovations & Patient Support in Radiation Oncology, Vol 23, Iss , Pp 15-20 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2405-6324 |
| DOI: |
10.1016/j.tipsro.2022.08.001 |
| Popis: |
Purpose: A fully independent, machine learning-based automatic treatment couch parameters prediction was developed to support surface guided radiation therapy (SGRT)-based patient positioning protocols. Additionally, this approach also acts as a quality assurance tool for patient positioning. Materials/Methods: Setup data of 183 patients, divided into four different groups based on used setup devices, was used to calculate the difference between the predicted and the acquired treatment couch value. Results: Couch parameters can be predicted with high precision μ=0.90,σ=0.92. A significant difference (p 1.5 cm), patient setup has to be verified to optimally use the surface scanning system. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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