TY - JOUR
T1 - CycleGan-based kV-to-MV image translation for potential in vivo dosimetry application
T2 - concept proposal and cross-institutional validation
AU - Qu, Rui
AU - Song, Zhexiang
AU - Lu, Qijian
AU - Hu, Huimin
AU - Dong, Zhengkun
AU - Yu, Shutong
AU - Liu, Jiang
AU - Deng, Juan
AU - Mei, Guojian
AU - Yin, Chuou
AU - Li, Qiao
AU - Xiang, Fei
AU - Li, Tian
AU - Lin, Chen
AU - Qi, Junfeng
AU - Le, Xiaoyun
AU - Zhang, Yibao
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2026/12
Y1 - 2026/12
N2 - 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.
AB - 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.
KW - CycleGAN
KW - Image guided radiotherapy
KW - In vivo dose
KW - Quality assurance
KW - Transmitted dosimetry
UR - https://www.scopus.com/pages/publications/105027472334
U2 - 10.1186/s12880-025-02111-3
DO - 10.1186/s12880-025-02111-3
M3 - 文章
C2 - 41366356
AN - SCOPUS:105027472334
SN - 1471-2342
VL - 26
JO - BMC Medical Imaging
JF - BMC Medical Imaging
IS - 1
M1 - 28
ER -