Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Xing, Yixun; Nguyen, Dan; Lu, Weiguo; Yang, Ming; Jiang, Steve

Xing, Yixun; Nguyen, Dan; Lu, Weiguo; Yang, Ming; Jiang, Steve.

Affiliation

Xing Y; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Nguyen D; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Lu W; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Yang M; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Jiang S; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Med Phys ; 47(2): 753-758, 2020 Feb.

Article de En | MEDLINE | ID: mdl-31808948

Sujet(s)
Mots clés

deep learning; dose calculation; radiotherapy

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Prostate / Planification de radiothérapie assistée par ordinateur / Radiothérapie conformationnelle avec modulation d'intensité / Apprentissage profond Type d'étude: Prognostic_studies Limites: Humans / Male Langue: En Journal: Med Phys Année: 2020 Type de document: Article Pays d'affiliation: États-Unis d'Amérique Pays de publication: États-Unis d'Amérique

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