The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]), the CNN (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]) and the GAN (SSIM: p [Formula: see text]; PSNR: p <0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Even with highly conformal treatment delivery techniques for radiation therapy, one cannot avoid the irradiation of healthy tissues surrounding the tumor. In treatment planning, absorbed dose constraints are defined for organs at risk visible in the patient's computed tomography scan in order to minimize side effects. However, scattered or secondary absorbed dose is also deposited in areas outside of the imaged volume. Although these doses are typically quite low (well below 1% of the therapeutic dose), there is the potential risk for patients to develop a radiation-induced second malignancy later in life. For a systematic analysis of scattered or secondary doses and their carcinogenic effects, accurate patient-specific organ dosimetry is necessary. Computational patient models are needed because low absorbed doses to organs away from the primarily irradiated target area cannot be measured easily. Monte Carlo simulations can incorporate the geometry and tissue properties of computational phantoms to simulate dose deposition events weighted with radiation weighting factors. Epidemiological models can then be applied to relate organ equivalent absorbed doses to cancer risks. This paper describes the use of computational phantoms in conjunction with Monte Carlo dose calculations and epidemiological risk models. We demonstrate how these simulations are applied with the purpose of estimating second cancer risks in radiation therapy and of improving existing epidemiological risk models.