Learning‐based CBCT correction using alternating random forest based on auto‐context model

PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image‐guided radiotherapy because it provides a foundation for advanced image‐guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currentl...

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Detalhes bibliográficos
Publicado no:Med Phys
Main Authors: Lei, Yang, Tang, Xiangyang, Higgins, Kristin, Lin, Jolinta, Jeong, Jiwoong, Liu, Tian, Dhabaan, Anees, Wang, Tonghe, Dong, Xue, Press, Robert, Curran, Walter J., Yang, Xiaofeng
Formato: Artigo
Idioma:Inglês
Publicado em: John Wiley and Sons Inc. 2018
Assuntos:
QUANTITATIVE IMAGING AND IMAGE PROCESSING
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7792987/
https://ncbi.nlm.nih.gov/pubmed/30471129
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/mp.13295
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