| Project/Area Number |
17K09059
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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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| Research Field |
Medical Physics and Radiological Technology
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| Research Institution | Niigata University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
成田 啓廣 新潟大学, 医歯学系, 助教 (10770208)
和田 真一 新潟大学, 医歯学系, 教授 (80105519)
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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 |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | X線CT / 変調伝達関数（MTF） / 点広がり関数（PSF） / スライス感度分布（SSP） / 空間分解能 / 逐次近似再構成 / 肺がん / コンピュータ支援診断（CAD） / X線CT装置 / CT検診 / エックス線CT |
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| Outline of Final Research Achievements |
To evaluate the performance of computer-aided detection (CAD) of lung cancer CT screening, we proposed a new method for generating computer-simulated nodule images ‘virtual nodules’. The performance of a CAD system is affected by CT scanning and image reconstruction conditions. Therefore, CAD performance should be evaluated by image data obtained under the same CT conditions as those used at each site providing CT screening. The virtual nodules were generated based on the spatial resolution of a CT system used in an institution providing cancer screening and were fused into lung images obtained at that institution, allowing site-specific virtual nodules with a realistic appearance. In phantom experiments using laboratory-made physical nodules, the virtual nodules were confirmed to agree well with CT images obtained by scanning the phantom nodules. Virtual nodules can be an effective means of evaluating site-specific CAD performance.
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| Academic Significance and Societal Importance of the Research Achievements |
肺がんの早期発見のために胸部CT検診およびコンピュータ支援診断（CAD）の普及が必要である。しかし、CADによる肺がんの検出性能はCT画像の画質に大きく依存するため、それぞれの検診施設で個別に性能を評価しなければならない。本研究で開発したVirtual Noduleは各施設の画像の空間分解能や雑音の特性を持つ結節像であることから、CADの性能を各施設が適確に評価することが可能となる。これは従来のCAD性能評価法では実現できなかったものであり、肺がんCT検診の普及につながる。
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