Development of High-Accuracy Tumor Tracking Systems for Next-Generation Radi ation Therapy Technology
Project/Area Number |
17K17582
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Medical Physics and Radiological Technology
Life / Health / Medical informatics
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Research Institution | Sendai National College of Technology |
Principal Investigator |
Zhang Xiaoyong 仙台高等専門学校, 総合工学科, 助教 (90722752)
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Project Period (FY) |
2017-04-01 – 2020-03-31
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Project Status |
Completed (Fiscal Year 2019)
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Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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Keywords | 画像誘導放射線治療 / マーカレス追尾照射 / Radiation therapy / Tumor tracking / Hidden Markov model / IGRT / Radiographic imaging / Radiation Therapy / Markerless Tracking / Radiographic Imaging |
Outline of Final Research Achievements |
This research focused on developing a radiographic image tracking system to track the tumor motion in real-time for adaptive tumor following radiation therapy. In this research projects, we have developed a robust tracking system that is capable of tracking the tracking the tumor’s position and its boundary in radiographic images. The experimental results performed on the phantom and clinical data demonstrated that the effectiveness of the tracking systems for clinical application.
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Academic Significance and Societal Importance of the Research Achievements |
放射線治療において,治療効果を高めつつ副作用を避けるためには,病巣周辺の健康な組織への照射を極力避け,腫瘍のみへの正確な照射が要求される。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や形状などを自動的にリアルタイムに把握することは,正常組織障害の低減の同時に、放射線治療の精度を向上できると考えられる。
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Report
(4 results)
Research Products
(25 results)
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[Journal Article] Hidden Markov Model-based Extraction of Target Objects in X-ray Image Sequence for Lung Radiation Therapy2020
Author(s)
新藤 雅大, 市地 慶, 本間 経康, 張 曉勇, 奥田 隼梧, 杉田 典大, 八巻 俊輔, 髙井 良尋, 吉澤 誠
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Journal Title
IEEJ Transactions on Electronics, Information and Systems
Volume: 140
Issue: 1
Pages: 49-60
DOI
NAID
ISSN
0385-4221, 1348-8155
Year and Date
2020-01-01
Related Report
Peer Reviewed
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[Presentation] 深層学習による乳房X線画像上の腫瘤鑑別2017
Author(s)
鈴木真太郎, 張曉勇, 佐々木拓也, 本間経康, 市地慶, 魚住洋佑, 高根侑美, 柳垣聡, 川住祐介, 石橋忠司, 吉澤誠
Organizer
第11回コンピューテーショナル・インテリジェンス研究会
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