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Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net.

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第一作者： Shunyu,Yan

第一单位： The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

作者： Shunyu,Yan ^[1] ; Austen,Maniscalco ^[1] ; Biling,Wang ^[1] ; Dan,Nguyen ^[1] ; Steve,Jiang ^[1] ; Chenyang,Shen ^[1]

作者单位： The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America. ^[1]

关键词 artificial intelligence deep learning prostate cancer real-time calculation secondary dose verification

DOI 10.1088/2632-2153/ad829e

PMID 39399396

发布时间 2025-02-27

基金项目

R01 CA237269/CA/NCI NIH HHS/United States R01 CA254377/CA/NCI NIH HHS/United States R01 CA258987/CA/NCI NIH HHS/United States

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Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net.

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Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net.

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