Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy

Abstract

PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients

“…The results of the quantitative analysis of image quality show that the SSIM values of the cGAN and U-Net are higher than those of CycleGAN, as compared with 0.8911 from a study by Chen, 8 0.85 from a study by Liang, 15 and 0.81 from a study by Sun. 19 The opposite result was found in Liu's study, 20 where SSIM values based on U-Net were much lower than those of the CycleGAN method (0.56 vs 0.70). Some researchers have put forward this use of 3D CycleGAN as a way to improve the image quality of the pseudo-CT.…”

“…Maspero found that the results based on CycleGAN can be decreased from 195 HU to 51 HU, 16 and the MAE in the pelvic region for pseudo-CT images based on an improved 3D CycleGAN is above 50 HU. 19 Chen illustrated that the MAE of synthesized CT images based on U-Net was 18.98 HU, 8 and Li explained that the range of the MAE for U-Net improved to (6, 27) HU. 13 The CycleGAN requires the fake image to keep all the information in the original image; as a result, its CT correction ability was reduced to some degree.…”

“…The results of Sun showed that the SSIM increases from 0.81 to 0.86 using the 3D CycleGAN method for cervical cancer patients. 19 Liu found that the SSIM of synthesized CT images improved to 0.71 from using the 3D CycleGAN rather than the 2D CycleGAN. 20 …”

Technical note: Air bubble‐induced performance degradation in automatic rectum segmentation from cone‐beam CT

“…While automatic segmentation using deep networks has demonstrated promise, it has shown inferior performance based on CBCT than fan‐beam CT with similar networks 1,8,9 . In addition, we have observed in a recent study with 85 CBCT cases that rectum segmentation from postprostatectomy patients has been generally more challenging than from patients without surgery, even with comparable rectum morphology statistics 10 .…”

Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study

“…However, imaging artifacts caused by respiratory movement make CBCT-based adaptive radiotherapy for breast cancer infeasible. CBCT images cannot be directly used for dose calculation due to inaccurate HU values and needs to be converted into synthetic CT for dosimetric evaluation (16)(17)(18)(19).…”