Diffusion model-based contrast-enhanced CT synthesis for breast cancer radiotherapy: Pursuing contrast-free imaging

ObjectiveThe purpose of this study aims to develop a novel deep learning framework for exploring its application effect in the transition from non-contrast computed tomography (NCCT) to contrast-enhanced computed tomography (CECT) in breast cancer radiotherapy.Materials and methodsA total of 194 patients with a pair of NCCT and CECT including 176 patients from hospital one and 18 patients from hospital two were enrolled in this study which were divided into training cohort (122 patients), internal testing cohort (54 patients) and external validation cohort (18 patients). Pix2Pix, CycleGAN, RegGAN and SynDiff were used to develop image-to-image translation in this study. PSNR, SSIM and NMAE were applied to evaluate the performance of automatic models by comparing them with original in the three cohorts.ResultsThe SynDiff models achieved the highest PSNR values of 28.56 and 26.97 dB, the highest SSIM value of 0.943 and 0.940, the lowest NMAE value of 0.011 and 0.012 compared with Pix2Pix, CycleGan and RegGAN in the internal validation cohorts and external validation cohorts, respectively. The p-values of the Wilcoxon signed-rank test for the SynDiff model compared with the other three models in PSNR and NMAE were all less than 0.05 in the internal and external validation cohorts. The p-values of the Wilcoxon signed-rank test for the SynDiff model compared with the other three models in PSNR and NMAE were all less than 0.05 in the internal and external validation cohorts. The p-values of the Wilcoxon signed-rank test for the SynDiff model compared with Pix2Pix and CycleGAN in SSIM were all less than 0.05 and compared with RegGAN in SSIM were 0.091 in the internal validation cohorts and all less than 0.05 in the external cohorts.ConclusionSynDiff is a promising method to explore its application effect in the transition from NCCT to CECT in the breast-cancer radiotherapy.