Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.

Yamaguchi, Mitsutaka; Liu, Chih-Chieh; Huang, Hsuan-Ming; Yabe, Takuya; Akagi, Takashi; Kawachi, Naoki; Yamamoto, Seiichi

Yamaguchi, Mitsutaka; Liu, Chih-Chieh; Huang, Hsuan-Ming; Yabe, Takuya; Akagi, Takashi; Kawachi, Naoki; Yamamoto, Seiichi.

Afiliación

Yamaguchi M; Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science (QST), Takasaki, Japan.
Liu CC; Department of Biomedical Engineering, University of California, Davis, CA, USA.
Huang HM; Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan.
Yabe T; Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Akagi T; Hyogo Ion Beam Medical Center, Tatsuno, Japan.
Kawachi N; Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science (QST), Takasaki, Japan.
Yamamoto S; Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.

Med Phys ; 47(8): 3520-3532, 2020 Aug.

Article en En | MEDLINE | ID: mdl-32335924

Asunto(s)

Aprendizaje Profundo; Electrones; Carbono; Flujo de Trabajo; Rayos X

Palabras clave

carbon ion; deep learning; dose; range; secondary electron bremsstrahlung x ray; simulation

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Electrones / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Med Phys Año: 2020 Tipo del documento: Article País de afiliación: Japón

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