Development of a new MRI image reconstruction by the fusion of compressed sensing and deep learning
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 |
Review Section |
Medium-sized Section 90:Biomedical engineering and related fields
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Research Institution | Kyoto University |
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 |
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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Report
(4 results)
Research Products
(28 results)
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[Journal Article] Evaluation of image quality of pituitary dynamic contrast‐enhanced MRI using time‐resolved angiography with interleaved stochastic trajectories (TWIST) and iterative reconstruction TWIST (IT‐TWIST)2020
Author(s)
Yokota Y, Fushimi Y, Okada T, Fujimoto K, Oshima S, Nakajima S, Fujii T, Tanji M, Inagaki N, Miyamoto S, Togashi K
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Journal Title
Journal of Magnetic Resonance Imaging
Volume: 51
Issue: 5
Pages: 1497-1506
DOI
NAID
Related Report
Peer Reviewed
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[Journal Article] Complementary regional heterogeneity information from COPD patients obtained using oxygen-enhanced MRI and chest CT2018
Author(s)
Fuseya Y, Muro S, Sato S, Tanabe N, Sato A, Tanimura K, Hasegawa K, Uemasu K, Kubo T, Kido A, Fujimoto K, Fushimi Y, Kusahara H, Sakashita N, Ohno Y, Togashi K, Mishima M, Hirai T.
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Journal Title
PLoS One
Volume: 30
Issue: 8
Pages: e0203273-e0203273
DOI
NAID
Related Report
Peer Reviewed / Open Access
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[Journal Article] Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and experts' knowledge.2018
Author(s)
Kawagishi M, Kubo T, Sakamoto R, Yakami M, Fujimoto K, Aoyama G, Emoto Y, Sekiguchi H, Sakai K, Iizuka Y, Nishio M, Yamamoto H, Togashi K.
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Journal Title
PLoS One
Volume: 16
Issue: 11
Pages: e0207661-e0207661
DOI
NAID
Related Report
Peer Reviewed / Open Access
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[Journal Article] Does the Diffusion Tensor Model Predict the Neurite Distribution of Cerebral Cortical Gray Matter? ? Cortical DTI-NODDI2018
Author(s)
Hikaru Fukutomi, Matthew F. Glasser, Katsutoshi Murata, Thai Akasaka, Koji Fujimoto, Takayuki Yamamoto, Joonas A. Autio, Tomohisa Okada, Kaori Togashi, Hui Zhang, David C. Van Essen, Takuya Hayashi
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Journal Title
DOI
Related Report
Open Access / Int'l Joint Research
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[Presentation] Advantages and pitfalls in the imaging with human 7T MRI scanner2019
Author(s)
Koji Fujimoto, Tomohisa Okada, Dinh Ha Duy Thuy, Toru Ishii, Martijn A. Cloos, Yuta Urushibata, Hideto Kuribayashi, Tobias Kober, Nouha Salibi, Ravi Seethamraju, John Grinstead, Tadashi Isa
Organizer
2019 RSNA
Related Report
Int'l Joint Research
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[Presentation] Compressed Sensing MR Angiography from Simulation Research to Clinical Practices2019
Author(s)
Sayo Otani, Yasutaka Fushimi, Koji Fujimoto, Takayuki Yamamoto, Azusa Ota, Krishna W. Pandu, Sonoko Oshima, Yusuke Yokota, Satoshi Nakajima, Akihiko Sakata, Akira Yamamoto, Peter Speier, Christoph Forman, Michaela Schmidt, Tomohisa Okada, Kaori Togashi
Organizer
2019 RSNA
Related Report
Int'l Joint Research
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