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Background <p>This work aims to develop and validate a novel CycleGan-based methodology to transfer the kV planning CT (pCT) to the reference MV portal images, potentially applicable to in vivo treatment dose monitoring.</p> Methods <p>The kV projections of pCT were prepared based on the various gantry angles of MV projections using treatment beams on Varian Halcyon system. A CycleGAN-based network incorporating attention module (ECA-CycleGAN) was trained to learn the relationship between kV and MV images, which performance was compared with the conventional Pix2pix and CycleGAN methods quantitatively. The beam angles and multi-leaf collimator parameters retrieved from the clinical plans were used to segment the treatment apertures on the model-generated reference MV images, within which the sensitivity to the artificial errors were tested using gamma analysis. Cross-institutional validations were performed on multiple machines and scanning protocols.</p> Results <p>Comparing the 2574 model-generated MV images with the measured ground truth of 13 validation cases, the mean ± standard deviation of the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) were 0.969 ± 0.007, 41.3 ± 3.2 and 1.6·10<sup>− 2</sup>±3.8·10<sup>− 4</sup> for ECA-CycleGAN, consistently better than that of using CycleGAN (0.940 ± 0.004, 35.7 ± 3.3 and 2.6·10<sup>− 2</sup>±4.5·10<sup>− 4</sup>) and pix2pix (0.931 ± 0.005, 32.4 ± 4.0 and 4.2·10<sup>− 2</sup>±4.8·10<sup>− 4</sup>) respectively. After introducing artificial translational or rotational errors, the gamma passing rates decreased and the gamma indices increased significantly (all <i>P</i> &lt; 0.05). The ECA-CycleGAN model displayed good generalizability across various pCT scanners, imaging protocols and Halcyon accelerators from 2 institutions.</p> Conclusion <p>Without complex and time-consuming Monte Carlo simulations, the proposed ECA-CycleGAN network facilitates the efficient establishment of the reference MV portal images applicable to in vivo transmitted dosimetry. It may potentially improve the accuracy of dose delivery especially for the advanced treatment techniques when pretreatment measurement verification or inter-fractional dose remediation are impossible.</p>

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CycleGan-based kV-to-MV image translation for potential in vivo dosimetry application: concept proposal and cross-institutional validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Rui Qu,
  • Zhexiang Song,
  • Qijian Lu,
  • Huimin Hu,
  • Zhengkun Dong,
  • Shutong Yu,
  • Jiang Liu,
  • Juan Deng,
  • Guojian Mei,
  • Chuou Yin,
  • Qiao Li,
  • Fei Xiang,
  • Tian Li,
  • Chen Lin,
  • Junfeng Qi,
  • Xiaoyun Le,
  • Yibao Zhang

摘要

Background

This work aims to develop and validate a novel CycleGan-based methodology to transfer the kV planning CT (pCT) to the reference MV portal images, potentially applicable to in vivo treatment dose monitoring.

Methods

The kV projections of pCT were prepared based on the various gantry angles of MV projections using treatment beams on Varian Halcyon system. A CycleGAN-based network incorporating attention module (ECA-CycleGAN) was trained to learn the relationship between kV and MV images, which performance was compared with the conventional Pix2pix and CycleGAN methods quantitatively. The beam angles and multi-leaf collimator parameters retrieved from the clinical plans were used to segment the treatment apertures on the model-generated reference MV images, within which the sensitivity to the artificial errors were tested using gamma analysis. Cross-institutional validations were performed on multiple machines and scanning protocols.

Results

Comparing the 2574 model-generated MV images with the measured ground truth of 13 validation cases, the mean ± standard deviation of the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) were 0.969 ± 0.007, 41.3 ± 3.2 and 1.6·10− 2±3.8·10− 4 for ECA-CycleGAN, consistently better than that of using CycleGAN (0.940 ± 0.004, 35.7 ± 3.3 and 2.6·10− 2±4.5·10− 4) and pix2pix (0.931 ± 0.005, 32.4 ± 4.0 and 4.2·10− 2±4.8·10− 4) respectively. After introducing artificial translational or rotational errors, the gamma passing rates decreased and the gamma indices increased significantly (all P < 0.05). The ECA-CycleGAN model displayed good generalizability across various pCT scanners, imaging protocols and Halcyon accelerators from 2 institutions.

Conclusion

Without complex and time-consuming Monte Carlo simulations, the proposed ECA-CycleGAN network facilitates the efficient establishment of the reference MV portal images applicable to in vivo transmitted dosimetry. It may potentially improve the accuracy of dose delivery especially for the advanced treatment techniques when pretreatment measurement verification or inter-fractional dose remediation are impossible.