Background: As an imaging modality, cone beam CT (CBCT) is widely used in dentistry, which can help dentists to observe tissues such as roots and jaws without confusion. CBCT has the advantages of convenience and low radiation dose, however low contrast and large noise are the serious points in the images. These disadvantages make it extremely difficult for doctors to accurately identify target tissues. Due to the differences in scanning methods and reconstruction algorithms between CBCT and multi-row detector spiral CT (MDCT),the current CT noise reduction models have significant shortcomings when reduce the noise on CBCT images.
Methods: In this paper, we propose a method of image noise reduction based on conditional generative adversarial network to improve the quality of CBCT images. The normal-dose MDCT images are used as the ground truth images to train the model to generate the denoise images.
Results: In order to increase the model’s sensitivity to the gradient information, a gradient loss function is involved in our proposed method. The verification experiments on the simulated data set and the real data set show that our model effectively generates the denoise images as well as preserves the quality of the images.
Conclusions: We compared the denoising effect between our model and other models with different loss functions. The scores by PSNR, MS-SSIM and GMSD showed that our model had better edge characteristics and denoising effect.