Deep Image Prior Acceleration Method for Target Offset in Low-dose CT Images Denoising

Low Dose CT (LDCT) images can significantly reduce the X-ray radiation dose, but there is a lot of noise that affects doctors' diagnosis. Deep Image Prior (DIP) is an unsupervised deep learning algorithm that uses random tensor as the input of neural network and iterates with a single LDCT image as the target. However, DIP needs thousands of iterations to get the best denoised results, resulting in the slow running speed of this method. Therefore, a DIP acceleration method for target offset in low-dose CT images is proposed, which aims to improve the running speed while maintaining the quality of denoised image. According to the similarity of LDCT slice images of an organ (such as lungs), the algorithm associates independent networks whose target images are different slices by inheriting parameters, updates the network parameters corresponding to the current slice based on the network parameters corresponding to the previous slice, and takes the network parameters corresponding to the current slice as the basis of next network corresponding to next slice to update parameters; Since the input of DIP network is a fixed random tensor, which is different from the target image greatly, this paper uses the LDCT image preprocessed by the traditional models as the network input to improve further the network iteration speed. Experiments show that the proposed acceleration algorithm can improve the iteration speed by 10.45% compared with the original DIP network without traditional model preprocessing. When LDCT preprocessed by Relative Total Variation (RTV) model is used as the network input, the image peak signal-to-noise ratio can not only reach 29.13, but also the total iterative speed can be increased by 94.31%. Therefore, this algorithm can greatly improve the running speed while maintaining the denoised quality of DIP, especially when the CT image preprocessed by RTV model is used as the network input, the effect of improving the running speed is more obvious.