| Project/Area Number |
18K07692
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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 | Juntendo University |
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Principal Investigator |
Ozaki Yutaka 順天堂大学, 医学部, 教授 (60233516)
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| Co-Investigator(Kenkyū-buntansha) |
堀 正明 順天堂大学, 医学部, 客員准教授 (40334867)
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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: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | MRI / 3D-QALAS / 脳 / グリオーマ / 定量値 / synthetic MRI / 脳腫瘍 / AI |
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| Outline of Final Research Achievements |
Synthetic MRI allows the creation of arbitrary contrast-enhanced images by setting parameters for quantitative values obtained by quantitative MRI, which measures T1, T2 and proton densities. However, synthetic MRI of the brain had been performed only in 2D, not 3D. In this study, we established the reliability of quantitative values obtained with 3D synthetic MRI in order to apply them to analysis of gliomas. The quantitative values obtained with 3D-QALAS, which is a sequence of 3D synthetic MRI, showed high accuracy and repeatability, and then were accelerated by compressed sensing. We could accelerate the acquisition twice. In addition, 3D-QALAS was introduced into four MRI machines from three different companies to confirm the reproducibility of quantitative values. This study has established the basis for future AI analysis of glioma with 3D-QALAS because it is necessary to collect many different types of data and examine reproducibility in order to perform diagnosis using AI.
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| Academic Significance and Societal Importance of the Research Achievements |
3D synthetic MRIのシークエンスである3D-QALASで得られる定量値の高い正確性や反復性を示した後、compressed sensingで高速化。脳で11分11秒かかっていたのを、5分56秒と、約1/2の撮像時間まで加速することに成功した。また、3D-QALASを3つの異なる会社の4つのMRI機器に導入し、定量値の再現性を確認した。今回は基礎的検討のみとなってしまったが、AIを用いて診断を行うためには多数の異なる種類のデータを集めて再現性を検討しなければならないため、本研究により、今後3D-QALASにおいてグリオーマをAI解析する基盤が確立されたと考えられる。
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