| Project/Area Number |
16K01431
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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 systems
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| Research Institution | Kobe City College of Technology |
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Principal Investigator |
NAGATANI Yoshiki 神戸市立工業高等専門学校, その他部局等, 准教授 (60448769)
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| Research Collaborator |
MATSUKAWA Mami
CHIBA Ko
TAKI Hirofumi
OKUMURA Shigeaki
WU Shuqiong
AIBA Eriko
SAEKI Takashi
Haïat Guillaume
Nguyen Vu-Hieu
Naili Salah
Wear Keith A.
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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 |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 超音波 / 骨粗鬆症 / 骨密度 / 骨質 / 橈骨 / FDTD / シミュレーション / 機械学習 / FDTD法 / 人体内の音波伝搬 / 骨粗鬆症診断 / 海綿骨 / 皮質骨 |
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| Outline of Final Research Achievements |
A numerical simulation system of a real-size 3-D model of the human radius including the cancellous bone part inside for calculating the propagation of ultrasound was created by using a high-resolution CT device for deriving the X-ray image of human bone. As a result, it was realized to investigate the behavior of the ultrasound when the bone density or the physical parameters of the bone were changed.
The investigation using the visco-elastic FDTD method was also performed. Here, some interesting effects of the viscosity of bone material on the parameters of the received waveforms were found.
In addition, a machine learning system was developed to directly estimate not only the bone density but also the geometry of the wave reflectors allocated in the propagation field by using the received waveform that propagates inside the bone-mimicking model.
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| Academic Significance and Societal Importance of the Research Achievements |
研究計画立案当初は，手首内部の超音波伝搬の挙動の理解や得られたパラメータの検討をおこない，それらから骨粗鬆症診断装置の改良につなげることのみを目的としていた。この目的は充分に達成され，超高齢社会のQOL維持・向上のための貢献ができたと考えているが，これに加えて，機械学習を用いて骨密度を推定する手法や，伝搬経路内部の反射体の形状推定までがおこなえる可能性を示すことができた。これは新たな研究領域が生まれたことを意味しており，将来の超音波利用方法の拡大に大きく貢献するものであると考えている。以上のように，本研究課題の成果は当初の予定を超えた領域にまで広がっており，非常に意義深い多数の知見が得られた。
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