Scatter distribution estimated and corrected by using Convolutional Neural Network for multi-slice CT system

X-ray scatter is a major limit for good CT image quality. Apart from using hardware approach (e.g. anti-scatter grid), computational algorithms based on Monte-Carlo simulation or convolution kernels have been proven to be valid for compensating scatter effect. However, computational algorithms always have to take care about the balance between complexity and efficiency, so the performance has some limitation when scatter contribution is large. In this paper we proposed a deep learning based approach by adopting a convolutional neuro-network (CNN) to predict the scatter distribution on projection domain. The performance of the CNN-based model is validated in both projection domain as well as reconstructed image domain. The result shows that the scatter correction algorithm with learning approach is able to compensate the artifact from scatter radiations under various complicated scenarios, resulting in equivalent or even better image quality than commercially used kernel-based scatter correction algorithm.