Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow

Mitsutaka Yamaguchi, Chih Chieh Liu, Hsuan Ming Huang, Takuya Yabe, Takashi Akagi, Naoki Kawachi, Seiichi Yamamoto*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Purpose: 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. Methods: 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. Results: 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. Conclusions: We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

Original languageEnglish
Pages (from-to)3520-3532
Number of pages13
JournalMedical Physics
Volume47
Issue number8
DOIs
Publication statusPublished - 2020 Aug 1
Externally publishedYes

Keywords

  • carbon ion
  • deep learning
  • dose
  • range
  • secondary electron bremsstrahlung x ray
  • simulation

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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