Niigata University · Medical and Dental Hospital · 助教

Research keywordsradiotherapy physics・treatment planning・imaging dose・VMAT dosimetry・brain metastasis setup・algorithm comparison

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• The detection and classification of TF and DLG error were feasible for ; however, a considerable reduction of accuracy was observed for depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for and 1.0. The gamma analysis could not detect errors.論文
• Selection criteria for circular collimator- vs. Multileaf collimator-based plans in robotic stereotactic radiotherapy for brain metastases and benign intracranial disease: Impact of target size, shape complexity, and proximity to at-risk organs論文
• Journal of Contemporary Brachytherapy論文
• The purpose of this study was to create and evaluate deep learning‐based models to detect and classify errors of multi‐leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG).論文

• 放射線治療における臨床成績の向上を目指した線量不確かさ可視化技術の開発と臨床応用2025 · 研究課題/領域番号： 25K10906
• 歯の線量に着目した放射線う蝕のリスク因子解析およびリスク予測モデルの確立2023 · 研究課題/領域番号： 23K09454
• Delta-radiomicsを応用したVMAT患者個別QAシステムの開発2022 · 研究課題/領域番号： 22K07792
• 放射線治療における不確かさ要因を考慮した高精度線量評価システムの開発2021 · 研究課題/領域番号： 21K07722

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