An iterative sinogram metal artifact reducdion based on UNet

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient’s oral cavity, the CT images would show density discontinuities due to the metals’ “X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.