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.
Highlights
Cervical cancer is one of the most common gynecological malignant tumors
Rigid registration based on gray level or bone landmark is carried out through cone-beam CT (CBCT) images scanned before each treatment and CT images acquired at the simulation stage (CTsim) to formulate a radiotherapy plan
CTCycleGAN-3D, pseudo-CT images obtained based on 3D CycleGAN; CTCycleGAN-2D, pseudo-CT images obtained based on 2D CycleGAN
Summary
Cervical cancer is one of the most common gynecological malignant tumors. According to statistics released in the 2017 annual meeting of the European Society for Medical Oncology (ESMO), the frequency of new cervical cancer cases was fourth highest in female cancers, and its fatality rate was third-highest [1]. With the development of radiotherapy technology, image-guided radiation therapy (IGRT) has been gradually applied to clinical treatment of cervical cancer [3]. In comparison with diagnostic CT, cone-beam CT (CBCT), a commonly used image-guided device, has higher spatial resolution, so it can be used for beam position verification of a patient between fractionated treatments [4]. CBCT and standard multi-slice CT images are grayscale images that are processed and reconstructed by a computer after X-ray passes through different density tissues and organs, and the radiation energy after X-ray attenuation is measured by a flat panel detector. CBCT images need to be modified to meet the requirements of clinical treatment [6]
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