保健衛生学部 リハビリテーション学科
Profile Information
- Affiliation
- Professor (Professor and Chairman), School of Medicine, Faculty of Medicine, Fujita Health University
- Degree
- MD, PhD(Mar, 1998, Kobe University Graduate School of Medicine)
- Contact information
- yohno
fujita-hu.ac.jp - ORCID ID
https://orcid.org/0000-0002-4431-1084- J-GLOBAL ID
- 200901037501461104
- researchmap Member ID
- 1000372100
Research Interests
6Research Areas
2Research History
3-
Apr, 2019 - May, 2023
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Apr, 2012 - Mar, 2019
Education
1-
- Mar, 1998
Committee Memberships
28-
Oct, 2024 - Present
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Jun, 2024 - Present
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Sep, 2022 - Present
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Sep, 2020 - Present
Awards
42Papers
351-
Respiratory Investigation, Sep, 2026
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Japanese Journal of Radiology, Aug 29, 2026
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Journal of Magnetic Resonance Imaging, Jun, 2026
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Japanese journal of radiology, May 20, 2026PURPOSE: Since the clinical application of computed tomography (CT), cardiac and respiratory motion artifacts have caused decreased image quality and reduced detection or quantitative or qualitative evaluation of lung parenchymal or vascular abnormalities on chest CT with lung window settings in patients with pulmonary diseases. Recently, a deep learning (DL)-based motion correction algorithm (CLEAR Motion) has been developed and clinically used for chest CT. We hypothesized that CLEAR Motion can significantly reduce motion artifacts on chest CT examinations relative to conventional chest CT images reconstructed without CLEAR Motion. The purpose of this study was to determine the utility of CLEAR Motion for image quality improvement in chest CT with lung window settings in patients with various pulmonary diseases. MATERIALS AND METHODS: Fifty-six consecutive patients with various thoracic diseases underwent non-electrocardiogram-gated chest helical CT examination using a 320-detector row CT and underwent reconstruction using the conventional reconstruction method and CLEAR Motion. To compare the quantitative image quality, the cardio-pulmonary edge distance (CPED) and slope (CPES) were measured on each CT scan in the axial plane. Comparing cardiac motion reduction capability, overall image quality, cardiac motion artifact, and region conspicuity were visually assessed in the lung window setting on the axial, coronal, and sagittal planes. The paired t-test and Wilcoxon signed-rank test were then performed. RESULTS: The CPEDs and CPESs of the entire lung and left lung on CT with CLEAR Motion were significantly superior to those of CT without CLEAR Motion (p < 0.001). The overall image quality, cardiac motion artifact, and region conspicuity on CT with CLEAR Motion were significantly higher than those without CLEAR Motion on each plane (p < 0.001). CONCLUSION: The DL-based motion correction algorithm named as 'CLEAR Motion' has a potential to improve image quality on chest CT with lung window setting in patients with pulmonary diseases.
Misc.
644Books and Other Publications
25Presentations
800-
The 6th International Congress on Magnetic Resonance Imaging (ICMRI 2018) and 23rd Scientific Meeting of KSMRM, Mar, 2018
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第25回日本CT検診学会学術集会, Feb, 2018, 日本CT検診学会 Invited
Teaching Experience
1-
イメージング (神戸大学)
Professional Memberships
18Research Projects
22-
Grants-in-Aid for Scientific Research, Japan Society for the Promotion of Science, Apr, 2025 - Mar, 2028
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Grants-in-Aid for Scientific Research, Japan Society for the Promotion of Science, Apr, 2025 - Mar, 2028
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科学研究費助成事業, 日本学術振興会, Apr, 2023 - Mar, 2026
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科学研究費助成事業, 日本学術振興会, Apr, 2022 - Mar, 2025
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科学研究費助成事業, 日本学術振興会, Apr, 2021 - Mar, 2024