Development of a computer-aided device for ultra-low-dose CT screening for lung cancer and differential diagnosis of lung nodules using AI
| Project/Area Number |
18K07675
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Kobe University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
大野 良治 藤田医科大学, 医学部, 教授 (30324924)
吉川 武 神戸大学, 医学研究科, 特命講師 (40332788)
関 紳一郎 神戸大学, 医学研究科, 特命助教 (30773519)
西村 善博 神戸大学, 医学研究科, 名誉教授 (20291453)
眞庭 謙昌 神戸大学, 医学研究科, 教授 (50362778)
岸田 雄治 神戸大学, 医学部附属病院, 特命助教 (90792250)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 放射線医学 / CT / 人工知能 / 被曝線量低減 / 画像診断支援 / 肺癌検診 / 低線量CT / AI / Deep Learning |
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| Outline of Final Research Achievements |
In this research project, we developed computer-aided volumetry software with atrificial intelligence and compared it's capability with that without artificial intelligence. In addition, we tested CT value accuracy of standard-, rduced- and ultra-low-dose CTs reconstructed with different reconstruction methods with artificial intelligence.
These results were presented at international and domestic society meetings and published in international journals.
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| Academic Significance and Societal Importance of the Research Achievements |
1990年代の後半より臨床応用されてきたMDCTにおいて、近年臨床応用が進められている逐次近似再構成および逐次再構成などの新たな再構成法により低線量CTや超低線量CTの臨床応用が進められている。あわせて、NLSTによるCT肺癌検診の有用性及び日本肺癌学会による肺癌取扱い規約第8版における肺癌内のすりガラス濃度部分および充実部分の成分分析による病期分類や予後の差などが指摘されている。
本研究では人工知能を併用した新たなCAD装置の臨床応用を可能にするとともに新たな低線量CT検査を可能にした。
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Report
(5 results)
Research Products
(25 results)
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[Presentation] Ultra-High-Resolution and Area-Detector CTs for Lung Density Assessment: Comparison of Radiation Dose Reduction Capability among Hybrid-Type and Model-Based Iterative Reconstructions and Deep Learning Reconstruction at QIBA Recommended Phantom Study2021
Author(s)
Shigamura C. Ohno Y, Hamabuchi N, Watanabe A, Kataoka Y, Ida Y, Akino N, Ito Y, Kimata H, Fujii K, Nakanishi S, Murayama K, Katada K, Toyama H
Organizer
107th Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2021)
Related Report
Int'l Joint Research
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