WEKO3
-
RootNode
アイテム
Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow
https://repo.qst.go.jp/records/79976
https://repo.qst.go.jp/records/7997684197ca7-e950-40f8-8650-ab64527722dd
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-04-21 | |||||
| タイトル | ||||||
| タイトル | Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow | |||||
| 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× Chih-Chieh, Liu (UC Davis)× Hsuan-Ming, Huang (National Taiwan Univ.)× Takuya, Yabe (Nagoya Univ.)× Takashi, Akagi (Hyogo Ion Beam Medical Center)× Kawachi, Naoki× Seiichi, Yamamoto (Nagoya Univ.)× Yamaguchi, Mitsutaka× Kawachi, Naoki |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Imaging of the secondary electron bremsstrahlung (SEB) X-rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB X-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the X-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB X-ray images. To overcome these limitations of the SEB X-ray images measured by the X-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon-ion beam on the measured SEB X-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB X-ray and dose image pairs were generated by our in-house developed model function for different carbon-ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB X-ray to dose image conversion and super-resolution. After the network being trained with these simulated X-ray and dose image pairs, the dose images were predicted from simulated and measured SEB X-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10^-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB X-ray images, the MSE was no worse than 5.5 × 10^-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach. | |||||
| 書誌情報 |
Medical Physics 巻 47, 号 8, p. 3520-3532, 発行日 2020-04 |
|||||
| 出版者 | ||||||
| 出版者 | Wiley | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0094-2405 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1002/mp.14205 | |||||
| 関連サイト | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1002/mp.14205 | |||||
Share
Cite as
Yamaguchi, Mitsutaka, Chih-Chieh, Liu (UC Davis), Hsuan-Ming, Huang (National Taiwan Univ.), Takuya, Yabe (Nagoya Univ.), Takashi, Akagi (Hyogo Ion Beam Medical Center), Kawachi, Naoki, Seiichi, Yamamoto (Nagoya Univ.), Yamaguchi, Mitsutaka, Kawachi, Naoki, n.d., Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow: Wiley, 3520–3532 p.
Loading...
