• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

FUNAYAMA Satoshi  舟山 慧

ORCIDConnect your ORCID iD *help
… Alternative Names

Funayama Satoshi  舟山 慧

Less
Researcher Number 40790449
Other IDs
Affiliation (Current) 2025: 浜松医科大学, 医学部附属病院, 診療助教
Affiliation (based on the past Project Information) *help 2025: 浜松医科大学, 医学部附属病院, 診療助教
2022 – 2023: 浜松医科大学, 医学部附属病院, 診療助教
2021: 山梨大学, 大学院総合研究部, 特任助教
2020: 山梨大学, 医学部附属病院, 医員
Review Section/Research Field
Principal Investigator
Basic Section 52040:Radiological sciences-related
Except Principal Investigator
Basic Section 52040:Radiological sciences-related
Keywords
Principal Investigator
画像再構成 / 深層学習 / 肝臓 / MRI / 自由呼吸下撮像 / Gd-EOB-DTPA / 機械学習 / 人工知能 / QSM / 定量的磁化率マッピング … More
Except Principal Investigator
… More インターベンショナルラジオロジー / 複合現実 / 多核種 / MRI Less
  • Research Projects

    (4 results)
  • Research Products

    (6 results)
  • Co-Researchers

    (9 People)
  •  Clinical research model building for multi-nuclei MRI analysis of tissue metabolism

    • Principal Investigator
      五島 聡
    • Project Period (FY)
      2025 – 2027
    • Research Category
      Grant-in-Aid for Scientific Research (B)
    • Review Section
      Basic Section 52040:Radiological sciences-related
    • Research Institution
      Hamamatsu University School of Medicine
  •  The Development of Remote Interventional Radiology Assist System using Mixed Reality

    • Principal Investigator
      棚橋 裕吉
    • Project Period (FY)
      2025 – 2027
    • Research Category
      Grant-in-Aid for Scientific Research (C)
    • Review Section
      Basic Section 52040:Radiological sciences-related
    • Research Institution
      Hamamatsu University School of Medicine
  •  モデルベース深層学習と体動モデルを融合した自由呼吸下腹部MRI再構成手法の開発Principal Investigator

    • Principal Investigator
      舟山 慧
    • Project Period (FY)
      2023 – 2025
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 52040:Radiological sciences-related
    • Research Institution
      Hamamatsu University School of Medicine
  •  Development and clinical evaluation of fast quantitative susceptibility mapping for the liver using deep learning and compressed sensingPrincipal Investigator

    • Principal Investigator
      Funayama Satoshi
    • Project Period (FY)
      2020 – 2022
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 52040:Radiological sciences-related
    • Research Institution
      Hamamatsu University School of Medicine
      University of Yamanashi

All 2024 2023 2022 2021 2020

All Journal Article Presentation

  • [Journal Article] Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging2023

    • Author(s)
      Funayama S, Motosugi U, Ichikawa S, Morisaka H, Omiya Y, Onishi H
    • Journal Title

      MRMS

      Volume: 22 Issue: 4 Pages: 515-526

    • DOI

      10.2463/mrms.mp.2021-0103

    • ISSN
      1347-3182, 1880-2206
    • Language
      English
    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PROJECT-20K16755
  • [Presentation] Deep learning accelerated ultra-fast multi-phase acquisition improves the successful rate of arterial phase of gadoxetic acid-enhanced MRI2024

    • Author(s)
      Masaya Kutsuna, Satoshi Funayama, Tatsunori Kobayashi, Yukichi Tanahashi, Kumi Ozaki, Shintaro Ichikawa, Satoshi Goshima
    • Organizer
      第83回日本医学放射線学会総会
    • Data Source
      KAKENHI-PROJECT-23K14890
  • [Presentation] AI Zoo in Diagnostic Radiology2022

    • Author(s)
      Satoshi Funayama
    • Organizer
      FCA webinar in 東海
    • Invited
    • Data Source
      KAKENHI-PROJECT-20K16755
  • [Presentation] Meet the teacher13: AI Hands-on Let's try your AI in the MR image processing2022

    • Author(s)
      Satoshi Funayama
    • Organizer
      The 50th annual meeting of the japanese society for magnetic resonance in medicine
    • Invited
    • Data Source
      KAKENHI-PROJECT-20K16755
  • [Presentation] Model-based deep learning reconstruction using folded image training strategy (FITS) for abdominal 3D T1-weighted images2021

    • Author(s)
      Satoshi Funayama, Utaroh Motosugi, Shintaro Ichikawa, Hiroyuki Morisaka, Yoshie Omiya, Hiroshi Onishi
    • Organizer
      第49回日本磁気共鳴医学会大会
    • Data Source
      KAKENHI-PROJECT-20K16755
  • [Presentation] FITs-CNN: A Very Deep Cascaded Convolutional Neural Networks Using Folded Image Training Strategy for Abdominal MRI Reconstruction2020

    • Author(s)
      Satoshi Funayama, Tetsuya Wakayama, Hiroshi Onishi, and Utaroh Motosugi
    • Organizer
      ISMRM2020
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-20K16755
  • 1.  五島 聡 (90402205)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 2.  尾崎 公美 (00714651)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 3.  市川 新太郎 (20456479)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 4.  小林 龍徳 (40636958)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 5.  棚橋 裕吉 (40724563)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 6.  松尾 政之 (40377669)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 7.  兵藤 文紀 (10380693)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results
  • 8.  川田 紘資 (00585276)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results
  • 9.  井上 政則 (30338157)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results

URL: 

Are you sure that you want to link your ORCID iD to your KAKEN Researcher profile?
* This action can be performed only by the researcher himself/herself who is listed on the KAKEN Researcher’s page. Are you sure that this KAKEN Researcher’s page is your page?

この研究者とORCID iDの連携を行いますか?
※ この処理は、研究者本人だけが実行できます。

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi