| Project/Area Number |
18K18357
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 90110:Biomedical engineering-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Tomii Naoki 東京大学, 大学院医学系研究科(医学部), 助教 (00803602)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 医用画像再構成 / 深層学習 / 超音波計測 / 心電図計測 / 不整脈 / 心臓電極マッピング / 超音波診断 / in silico学習 / 膜電位動画再構成 / Bモード画像再構成 / 学習データセット構築 / 生体計測 |
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| Outline of Final Research Achievements |
This study aims to expand the application of simple biomedical measurement methods, such as ultrasound and bioelectrical measurements, to the precise diagnosis of various diseases, and to construct an image reconstruction method that is robust against inhomogeneities in the body by applying pattern recognition based on machine learning. As a result of training a deep neural network that reconstructs medical images from measurement signals with large-scale training data using numerical simulations for both ultrasound and ECG measurements, it was found that it is possible to reconstruct highly accurate medical images from limited measurement signals with higher precision than conventional methods.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究を通じて、深層学習によって従来より高画質な超音波計測が実現できる可能性が示された。これにより将来的に、現状では画質が限られる超音波画像診断をより精密な診断に応用できる可能性が開かれた。さらに、カテーテルを用いた心内心電図信号から、心臓内に発生する電気的興奮波を、興奮回復特性まで含めて従来よりも精密に可視化できる可能性が示された。これにより現状では治療の難しい複雑な不整脈に対し、正確な興奮状態の把握に基づく焼灼等の精密治療の可能性が開かれた。
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