Works matching AU Nayu Hamabuchi

Results: 8
    1

    Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT.

    Published in:
    European Radiology, 2024, v. 34, n. 2, p. 1065, doi. 10.1007/s00330-023-10105-4
    By:
    • Ohno, Yoshiharu;
    • Yui, Masao;
    • Yamamoto, Kaori;
    • Ikedo, Masato;
    • Oshima, Yuka;
    • Hamabuchi, Nayu;
    • Hanamatsu, Satomu;
    • Nagata, Hiroyuki;
    • Ueda, Takahiro;
    • Ikeda, Hirotaka;
    • Takenaka, Daisuke;
    • Yoshikawa, Takeshi;
    • Ozawa, Yoshiyuki;
    • Toyama, Hiroshi
    Publication type:
    Article
    2

    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.

    Published in:
    European Radiology, 2023, v. 33, n. 1, p. 368, doi. 10.1007/s00330-022-08983-1
    By:
    • Ohno, Yoshiharu;
    • Akino, Naruomi;
    • Fujisawa, Yasuko;
    • Kimata, Hirona;
    • Ito, Yuya;
    • Fujii, Kenji;
    • Kataoka, Yumi;
    • Ida, Yoshihiro;
    • Oshima, Yuka;
    • Hamabuchi, Nayu;
    • Shigemura, Chika;
    • Watanabe, Ayumi;
    • Obama, Yuki;
    • Hanamatsu, Satomu;
    • Ueda, Takahiro;
    • Ikeda, Hirotaka;
    • Murayama, Kazuhiro;
    • Toyama, Hiroshi
    Publication type:
    Article
    3
    4

    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.

    Published in:
    Acta Radiologica, 2022, v. 63, n. 10, p. 1363, doi. 10.1177/02841851211044973
    By:
    • Ohno, Yoshiharu;
    • Aoyagi, Kota;
    • Takenaka, Daisuke;
    • Yoshikawa, Takeshi;
    • Fujisawa, Yasuko;
    • Sugihara, Naoki;
    • Hamabuchi, Nayu;
    • Hanamatsu, Satomu;
    • Obama, Yuki;
    • Ueda, Takahiro;
    • Hattori, Hidekazu;
    • Murayama, Kazuhiro;
    • Toyama, Hiroshi
    Publication type:
    Article
    5

    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.

    Published in:
    Journal of Magnetic Resonance Imaging, 2023, v. 57, n. 1, p. 259, doi. 10.1002/jmri.28288
    By:
    • Ohno, Yoshiharu;
    • Yui, Masao;
    • Takenaka, Daisuke;
    • Yoshikawa, Takeshi;
    • Koyama, Hisanobu;
    • Kassai, Yoshimori;
    • Yamamoto, Kaori;
    • Oshima, Yuka;
    • Hamabuchi, Nayu;
    • Hanamatsu, Satomu;
    • Obama, Yuki;
    • Ueda, Takahiro;
    • Ikeda, Hirotaka;
    • Hattori, Hidekazu;
    • Murayama, Kazuhiro;
    • Toyama, Hiroshi
    Publication type:
    Article
    6

    Area-Detector Computed Tomography for Pulmonary Functional Imaging.

    Published in:
    Diagnostics (2075-4418), 2023, v. 13, n. 15, p. 2518, doi. 10.3390/diagnostics13152518
    By:
    • Ohno, Yoshiharu;
    • Ozawa, Yoshiyuki;
    • Nagata, Hiroyuki;
    • Bando, Shuji;
    • Cong, Shang;
    • Takahashi, Tomoki;
    • Oshima, Yuka;
    • Hamabuchi, Nayu;
    • Matsuyama, Takahiro;
    • Ueda, Takahiro;
    • Yoshikawa, Takeshi;
    • Takenaka, Daisuke;
    • Toyama, Hiroshi
    Publication type:
    Article
    7
    8

    Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

    Published in:
    Magnetic Resonance in Medical Sciences, 2024, v. 23, n. 4, p. 487, doi. 10.2463/mrms.mp.2023-0068
    By:
    • Daisuke Takenaka;
    • Yoshiyuki Ozawa;
    • Kaori Yamamoto;
    • Maiko Shinohara;
    • Masato Ikedo;
    • Masao Yui;
    • Yuka Oshima;
    • Nayu Hamabuchi;
    • Hiroyuki Nagata;
    • Takahiro Ueda;
    • Hirotaka Ikeda;
    • Akiyoshi Iwase;
    • Takeshi Yoshikawa;
    • Hiroshi Toyama;
    • Yoshiharu Ohno
    Publication type:
    Article