2022 Fiscal Year Final Research Report
Impact of dose uncertainty of tracking irradiation in three-dimensional measurements using a polymer-gel dosimetry with deep learning
Project/Area Number |
20K08097
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 52040:Radiological sciences-related
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Research Institution | National Cancer Center Japan |
Principal Investigator |
Hidenobu Tachibana 国立研究開発法人国立がん研究センター, 東病院, 室長 (20450215)
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Project Period (FY) |
2020-04-01 – 2023-03-31
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Keywords | ゲル線量計 / 深層学習 / 放射線治療 / MR |
Outline of Final Research Achievements |
The principles and implementation of deep learning for high resolution and noise reduction (DL-HRNR) have been completed, but it has been discovered that obtaining high-quality data is difficult and challenging. In the future, it is believed that obtaining training data for DL-HRNR can be achieved through high-speed sequences and low-noise sequences in MR imaging. Additionally, conducting MR imaging of gel dosimeters with various dose distribution patterns will be necessary to obtain a larger amount of training data.
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Free Research Field |
放射線治療医学物理学
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Academic Significance and Societal Importance of the Research Achievements |
深層学習が全てを解決できるわけではなく、よい教師データが必要であり、本研究におけるゲル線量計の教師データ取得が難しいことがわかった。 MRによるゲル線量計の画像化は歴史が古く、実績が多いため、デファクトスタンタードであり、信頼性も高いが、利便性の観点からすると、別のモダリティへのシフトが必要であると言える。
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