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Dive into the research topics where Yuki Obama is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors
Ikeda, H., Ohno, Y., Yamamoto, K., Murayama, K., Ikedo, M., Yui, M., Kumazawa, Y., Shimamura, Y., Takagi, Y., Nakagaki, Y., Hanamatsu, S., Obama, Y., Ueda, T., Nagata, H., Ozawa, Y., Iwase, A. & Toyama, H., 05-2024, In: Cancers. 16, 9, 1714.Research output: Contribution to journal › Article › peer-review
Open Access3 Link opens in a new tab Citations (Scopus) -
Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study
Ohno, Y., Akino, N., Fujisawa, Y., Kimata, H., Ito, Y., Fujii, K., Kataoka, Y., Ida, Y., Oshima, Y., Hamabuchi, N., Shigemura, C., Watanabe, A., Obama, Y., Hanamatsu, S., Ueda, T., Ikeda, H., Murayama, K. & Toyama, H., 01-2023, In: European Radiology. 33, 1, p. 368-379 12 p.Research output: Contribution to journal › Article › peer-review
8 Link opens in a new tab Citations (Scopus) -
Computed DWI MRI Results in Superior Capability for N-Stage Assessment of Non-Small Cell Lung Cancer Than That of Actual DWI, STIR Imaging, and FDG-PET/CT
Ohno, Y., Yui, M., Takenaka, D., Yoshikawa, T., Koyama, H., Kassai, Y., Yamamoto, K., Oshima, Y., Hamabuchi, N., Hanamatsu, S., Obama, Y., Ueda, T., Ikeda, H., Hattori, H., Murayama, K. & Toyama, H., 01-2023, In: Journal of Magnetic Resonance Imaging. 57, 1, p. 259-272 14 p.Research output: Contribution to journal › Article › peer-review
15 Link opens in a new tab Citations (Scopus) -
Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging
Ueda, T., Ohno, Y., Yamamoto, K., Murayama, K., Ikedo, M., Yui, M., Hanamatsu, S., Tanaka, Y., Obama, Y., Ikeda, H. & Toyama, H., 05-2022, In: Radiology. 303, 2, p. 373-381 9 p.Research output: Contribution to journal › Article › peer-review
132 Link opens in a new tab Citations (Scopus) -
Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations
Ohno, Y., Aoyagi, K., Takenaka, D., Yoshikawa, T., Fujisawa, Y., Sugihara, N., Hamabuchi, N., Hanamatsu, S., Obama, Y., Ueda, T., Hattori, H., Murayama, K. & Toyama, H., 10-2022, In: Acta Radiologica. 63, 10, p. 1363-1373 11 p.Research output: Contribution to journal › Article › peer-review
13 Link opens in a new tab Citations (Scopus)