Effects of Different Noise Reduction Deep Learning Strategies on Computed Tomography Images

Noise reduction in Computed Tomography (CT) is very important for two reasons. On one hand, it can improve the quality of reconstructions, and on the other hand, significant dose reduction can be achieved, which is important due to the radiation risk to the patient. In this area, deep learning-based noise reduction possibilities are in the focus of interest. There are numerous different network models with various training strategies. In this paper, we examined different supervised training strategies with four different deep-learning architectures, and for the training, we only used mathematical phantoms with artificially simulated noise. The noise model was designed for a real multi-slice cone-beam CT and was performed in projection space on forward-projected mathematical phantoms. We validated the noise model and evaluated the effectiveness of different deep learning-based noise reduction models. We redesigned some of the deep learning models and analyzed their noise reduction effect using the noise power spectrum. We examined the noise reduction performance of the models on both real multi-slice cone beam CT scans and mathematical phantoms, with particular attention to their practical applicability.