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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 |
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| 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 |
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| 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 | |||||
