| Project/Area Number |
16K19233
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Medical Physics and Radiological Technology
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Research Collaborator |
ARIMURA Hidetaka
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 高精度放射線治療 / 画像誘導放射線治療 / kV-CBCT画像 / 体幹部定位放射線治療 / テンプレートマッチング技術 / kV-CBCT 画像 / 肺定位放射線治療 / 放射線治療技術学 / 適応放射線治療 |
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| Outline of Final Research Achievements |
Author developed the automated estimation method of the location of tumor in kV-CBCT images. In the results, the proposed method could estimate the location of tumor less than 2 mm in kV-CBCT images. These results were reported in the magazine of medical imaging and information sciences.
Author examined that the proposed method apply to tumor region with rotation and form change. In addition, author considered that the proposed method was supported by machine learning algorithm for applying to organ at risk.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究における提案手法を用いて，実際に放射線治療を行っている際の腫瘍位置を自動的に推定することは，その放射線治療の精度を確認することに繋がり，放射線治療の適切な対象に十分な放射線を投与する一助となる．また，提案手法を基礎として，治療時の腫瘍の位置や状態によって日々の放射線治療を最適化できる可能性が示され，それが実現することで局所腫瘍制御率の向上や正常組織障害の低減につながると考えられる．
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