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Establishment of a method for estimating stopping power ratio in the patient's body for adaptive particle therapy

Research Project

Project/Area Number 19K17192
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 52040:Radiological sciences-related
Research InstitutionTokyo Women's Medical University (2022)
Yamagata University (2019-2021)

Principal Investigator

Kanai Takayuki  東京女子医科大学, 医学部, 講師 (40764139)

Project Period (FY) 2019-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Keywords放射線治療 / 粒子線治療 / 適応放射線治療 / MR画像誘導放射線治療 / 畳み込みニューラルネットワーク / Cone-beam CT / 体表面画像誘導 / 重粒子線治療 / 阻止能比 / 適応粒子線治療
Outline of Research at the Start

粒子線治療は、現在の主流であるX線治療と比べ、腫瘍に限局した線量分布を作成することが可能である一方、治療期間中の患者の体型変化や腫瘍の縮小・増大、及び照射途中に生じる体内臓器の移動による影響を受けやすい。
本研究は、治療期間中及び照射途中に生じるこれらの変化を考慮する「適応粒子線治療」の実現に向け、cone-beam CT (CBCT) 画像及びMRI画像から、粒子線の線量分布計算に不可欠である阻止能比を推定する手法の確立を目的とする。

Outline of Final Research Achievements

In order to establish a method for estimating the stopping power ratio for "adaptive particle radiotherapy", we developed a method using convolutional neural networks. We compared U-net, CycleGAN, and pix2pix and found that pix2pix was the best in terms of accuracy and applicability. The proposed method was accurate enough to be applied to tumors in a shallow region, and conversion time was about 1.3 seconds per case, which is fast enough to be applied to the on-line dose calculation.
For reducing the range uncertainty, we also developed a method to utilize a body surface image guidance system. The developed method was able to quantitatively calculate the magnitude of deformation of the patient's body surface with a high accuracy of less than 0.1 mm.

Academic Significance and Societal Importance of the Research Achievements

本研究で開発した手法は、患者の体型変化や臓器の変化などに応じて粒子線治療計画を変更する「適応粒子線治療」の実現に寄与すると期待される。また、粒子線の飛程と飛程誤差が比例関係でなく2乗根に比例する関係であったことはこれまでの研究報告とは異なる結果であり、今後の粒子線治療における飛程誤差の考え方に影響を及ぼすものである。更に、本研究の研究成果は学術大会のインターナショナルセッションで学術的な賞を受賞するなど、高い評価を受けている。

Report

(5 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • 2020 Research-status Report
  • 2019 Research-status Report
  • Research Products

    (5 results)

All 2022 2019

All Journal Article (2 results) (of which Peer Reviewed: 1 results) Presentation (3 results) (of which Int'l Joint Research: 1 results,  Invited: 1 results)

  • [Journal Article] Development of a quantitative analysis method for assessing patient body surface deformation using an optical surface tracking system2022

    • Author(s)
      Sato K, Sato R, Goto N, Kawamura T, Kanai T, Miyasaka Y, Lee SH, Souda H, Iwai T
    • Journal Title

      Radiological Physics and Technology

      Volume: 30 Issue: 4 Pages: 367-378

    • DOI

      10.1007/s12194-022-00676-0

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed
  • [Journal Article] AIを用いたCT画像合成と放射線治療への活用ーMR画像誘導粒子線治療へ向けてー2022

    • Author(s)
      金井貴幸
    • Journal Title

      Precision Medicine

      Volume: 5 Pages: 41-44

    • Related Report
      2021 Research-status Report
  • [Presentation] Carbon Ion Therapy with Superconducting Rotating Gantry at Yamagata University2022

    • Author(s)
      Takayuki Kanai
    • Organizer
      The 100th Anniversary Yonsei Radiation Oncology Symposium
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] Range uncertainties for MRI-only treatment planning with convolutional neural network in particle therapy2022

    • Author(s)
      Takayuki Kanai, Shinya Arai, Yuya Miyasaka, Hikaru Souda, Sung Hyun Lee, Hongbo Chai, Takeo Iwai, Kenji Nemoto
    • Organizer
      The 123rd Scientific Meeting of the Japan Society of Medical Physics
    • Related Report
      2021 Research-status Report
  • [Presentation] 畳み込みニューラルネットワークを用いたMRI画像から炭素線阻止能への変換法の検討2019

    • Author(s)
      荒井眞哉, 金井貴幸, 岩井岳夫, 想田光, 宮坂友侑也, 家子義朗, 根本建二
    • Organizer
      日本放射線腫瘍学会第32回学術大会
    • Related Report
      2019 Research-status Report

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Published: 2019-04-18   Modified: 2024-01-30  

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