Pseudo CT Images Synthesized from CBCT Based on Unsupervised DDPM in Cervical Cancer Radiotherapy

Abstract

The low image quality and inaccurate HU value of cone beam computed tomography (CBCT) restrict its further application in cervical cancer radiotherapy. A new unsupervised based denoising diffusion probabilistic model (DDPM) was proposed to synthesize pseudo-CT images from CBCT. CBCT and CT images of 120 patients with cervical cancer were selected. The proposed DDPM network with condition and iterative mechanism was used for data training and testing between two image domains. In the training process, the model first obtained coarse pseudo-CT images with Gaussian noise through a diffusion process. Then, with the real CT images as the training target, the noise images were nonlinearly mapped to the domain of the CT images through the inverse diffusion process, and the fine pseudo-CT images were obtained. We repeated the above steps and gradually increased the number of noise diffusion. When the image difference value is less than the threshold, the training of the model is terminated and the pseudo-CT images are output. In the testing stage, each pseudo-CT generated was compared against the real CT image of the same patient based on the metrics of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and normalized mean absolute error (NMAE). In terms of anatomical verification, the PSNR (dB), SSIM (%) and NCC values between the pseudo-CT images obtained based on DDPM and the real CT images were presented as mean (standard deviation), which were 31.92(0.46), 86.69(4.55) and 0.0106(0.002) respectively. Compared with CBCT, the accuracy of the three metric values has been improved 15.6%, 14.2% and 23.3% respectively. For the metric values of the pseudo-CT images obtained based on the U-Net and CycleGAN models, the results synthesized based on the proposed model were paired with T-tests, the p values were all less than 0.05, and the differences were statistically significant. The pseudo-CT images obtained based on the DDPM were close to the real CT images in anatomy. The pseudo-CT images synthesized by the proposed DDPM network have good application prospects in cervical cancer radiotherapy.