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. 2023 Apr 24;13(1):6624.
doi: 10.1038/s41598-023-33472-w.

CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma

Chen Jihong #  1 , Quan Kerun #  2 , Chen Kaiqiang  1 , Zhang Xiuchun  1 , Zhou Yimin  3 , Bai Penggang  4
Affiliations

CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma

Chen Jihong et al. Sci Rep. .

Abstract

This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error (MAE) were used to quantify the image quality. For validations, the contours and treatment plans in CT images were transferred to original CBCT, CBCT_cor, SCT1 and SCT2 for dosimetric comparison. Dose distribution, dosimetric parameters and 3D gamma passing rate were analyzed. Compared with rigidly registered CT (RCT), the MAE of CBCT, CBCT_cor, SCT1 and SCT2 were 346.11 ± 13.58 HU, 145.95 ± 17.64 HU, 105.62 ± 16.08 HU and 83.51 ± 7.71 HU, respectively. Moreover, the average dosimetric parameter differences for the CBCT_cor, SCT1 and SCT2 were 2.7% ± 1.4%, 1.2% ± 1.0% and 0.6% ± 0.6%, respectively. Using the dose distribution of RCT images as reference, the 3D gamma passing rate of the hybrid method was significantly better than the other methods. The effectiveness of CBCT-based sCT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma was confirmed. The image quality and dose accuracy of SCT2 were outperform the simple CycleGAN method. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Illustration of cycle-consistent generative adversarial network (CycleGAN).
Figure 2
Figure 2
(A) The side-by-side comparison of CBCT, CBCT_cor, RCT, SCT1 and SCT2 for a validation patient; (B) the line profile of line a; (C) the line profile of line b.
Figure 3
Figure 3
MAE and ME comparisons for each patient in validation cases. (A) MAE, (B) ME.
Figure 4
Figure 4
The dose distributions for three validation patients on CBCT_cor, RCT, SCT1 and SCT2 were displayed.
See this image and copyright information in PMC

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