Application of high-precision denoising technique with deep learning to neuroimaging research
Project/Area Number |
18K07712
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 52040:Radiological sciences-related
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Research Institution | Kyoto University |
Principal Investigator |
Oishi Naoya 京都大学, 医学研究科, 特定准教授 (40526878)
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Co-Investigator(Kenkyū-buntansha) |
藤原 宏志 京都大学, 情報学研究科, 准教授 (00362583)
鈴木 崇士 京都大学, 医学研究科, 特定助教 (10572224)
杉原 玄一 京都大学, 医学研究科, 助教 (70402261)
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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 |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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Keywords | 深層学習 / MRI / 脳 / 精神神経疾患 / ノイズ除去 / GPGPU / 医用画像工学 |
Outline of Final Research Achievements |
In order to improve the signal-to-noise ratio in neuroimaging research, we have newly developed a deep learning-based high-precision denoising algorithm for brain MRI. For small animals, the usefulness of denoising in morphological MRI of psychiatric model rats was clarified. Furthermore, by extending the method, we succeeded in improving the prognosis prediction performance of patients with neuropsychiatric disorders. Thus, we have clarified the usefulness of the deep learning-based high-precision denoising algorithm developed this time for both basic and clinical applications.
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Academic Significance and Societal Importance of the Research Achievements |
柔軟性・拡張性の高い深層学習ベースのノイズ除去アルゴリズムを新規に開発し、精神疾患モデルラットの形態MRIに応用することで従来検出しえなかった微小領域の変化を捉えることに成功した点は学術的意義が高いと考えられる。また、本手法を拡張することでMRIから縮約された情報を抽出させ、精神神経疾患患者の予後予測性能向上を果たした点は将来的な臨床応用という観点で社会的意義も高い。
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Report
(4 results)
Research Products
(35 results)
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[Journal Article] Deep learning-derived high-level neuroimaging features predict clinical outcomes for large vessel occlusion2020
Author(s)
Nishi H, *Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, Chihara H, Fukumitsu R, Okawa M, Yamana N, Imamura H, Sadamasa N, Hatano T, Nakahara I, Sakai N, Miyamoto S
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Journal Title
Stroke
Volume: 51
Issue: 5
Pages: 1484-1492
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning2019
Author(s)
Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, Chihara H, Fukumitsu R, Okawa M, Yamana N, Imamura H, Sadamasa N, Hatano T, Nakahara I, Sakai N, Miyamoto S
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Journal Title
Stroke
Volume: 50
Pages: 2379-2388
Related Report
Peer Reviewed / Open Access
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[Journal Article] Nigrostriatal Dopaminergic Dysfunction And Altered Functional Connectivity In REM Sleep Behaviour Disorder With Mild Motor Impairment2019
Author(s)
Yamada G, Ueki Y, Oishi N, Oguri T, Fukui A, Nakayama M, Kandori A, Sano Y, Kan H, Arai N, Sakurai K, Wada I, Matsukawa N
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Journal Title
Front. Neurol.
Volume: 10
Pages: 802-802
DOI
Related Report
Peer Reviewed / Open Access
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[Presentation] Predicting clinical outcomes of acute ischemic stroke due to large vessel occlusion: The approach to utilize neuroimaging data with deep learning2019
Author(s)
Hidehisa Nishi, Naoya Oishi, Akira Ishii, Isao Ono, Takenori Ogura, Tadashi Sunohara, Hideo Chihara, Ryu Fukumitsu, Masakazu Okawa, Norikazu Yamana, Hirotoshi Imamura, Nobutake Sadamasa, Taketo Hatano, Ichiro Nakahara, Nobuyuki Sakai,Susumu Miyamoto
Organizer
East Asian Conference on Neurointervention (EACoN) 2019
Related Report
Int'l Joint Research
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[Presentation] Predicting clinical outcomes of acute ischemic stroke due to large vessel occlusion: The approach to utilize high-dimensional neuroimaging data as a whole with deep learning2019
Author(s)
Hidehisa Nishi, Naoya Oishi, Akira Ishii, Isao Ono, Takehiro Katano, Yu Abekura, Hideo Chihara, Yukihiro Yamao, Masakazu Okawa, Takayuki Kikuchi, Taketo Hatano, Ichiro Nakahara, Susumu Miyamoto
Organizer
International Stroke Conference 2019
Related Report
Int'l Joint Research
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