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A simulation study on estimation of Bragg-peak shifts via machine learning using proton-beam images obtained by measurement of secondary electron bremsstrahlung
https://repo.qst.go.jp/records/77068
https://repo.qst.go.jp/records/770683a327b8d-e0a0-45d2-a9f6-11761495bf90
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-07-09 | |||||
| タイトル | ||||||
| タイトル | A simulation study on estimation of Bragg-peak shifts via machine learning using proton-beam images obtained by measurement of secondary electron bremsstrahlung | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Yamaguchi, Mitsutaka
× Yamaguchi, Mitsutaka× Nagao, Yuuto× Kawachi, Naoki× Yamaguchi, Mitsutaka× Nagao, Yuuto× Kawachi, Naoki |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | We investigated an estimation method of Bragg-peak shifts via machine learning using proton-beam images obtained by measurement of secondary electron bremsstrahlung (SEB) by Monte Carlo simulation. Proton beams having energy of 139 MeV were incident on a water phantom with randomly placed air spheres inside, and 6400 pairs of “proton-beam images” and “a Bragg-peak shift” were prepared and then multiple linear regression analysis was carried out. A good agreement was found between the actual Bragg-peak shifts and predicted values in both the training and test sets. The coefficients of determination of the obtained prediction model were 0.899 for the training set and 0.894 for the test set. Consequently, we found that a prediction model with small variance and high prediction performance could be obtained using the SEB data. | |||||
| 書誌情報 |
IEEE Transactions on Radiation and Plasma Medical Sciences 巻 4, 号 2, p. 253-261, 発行日 2020-03 |
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| 出版者 | ||||||
| 出版者 | IEEE Nuclear and Plasma Sciences Society | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 2469-7303 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1109/TRPMS.2019.2928016 | |||||
| 関連サイト | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1109/TRPMS.2019.2928016 | |||||
