| Project/Area Number |
18K19917
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 90:Biomedical engineering and related fields
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| Research Institution | Kyoto University |
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Principal Investigator |
Fujimoto Koji 京都大学, 医学研究科, 特定准教授 (10580110)
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| Project Period (FY) |
2018-06-29 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | 圧縮センシング / 深層学習 / compressed sensing / deep learning / MRI |
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| Outline of Final Research Achievements |
By using a measured data from radial scan MRI, simulated data with varying degree of time delay in the acquisition was generated. A correction method for this delay was reported at the international conference （international society for magnetic resonance in medicine, ISMRM 2020, abstract number #0657). For the deep learning network, a network consists of VGG19 and the following upsampling layers (Conv2DTranspose, Dropout, concatenate, BatchNormalization, Activation) was constructed and trained with 5000 knee MRI images, with a successful image recovery.
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| Academic Significance and Societal Importance of the Research Achievements |
画像再構成に深層学習の手法を融合させることができれば、画像再構成に要する時間の飛躍的な短縮がはかれ、臨床応用を加速する大きなポイントになりうる。深層学習を用いたMRI画像再構成研究はまだ萌芽期であり、ネットワークの構成、学習方法の最適化、画質評価の手法など今後も検討が必要である。画質劣化を伴わずにMRIの高速撮像が可能となれば、高齢者や小児など、臨床において長時間のMRI撮像が負担となる患者にとって、大きなメリットとなりうる。
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