3D FEW-VIEW CT IMAGE RECONSTRUCTION WITH DEEP LEARNING

Few-view CT imaging is an important approach to reduce the ionizing radiation dose. In this paper, we propose a three-dimensional (3D) deep-learning-based method for few-view CT image reconstruction directly from 3D projection data. The large memory requirement is a critical issue for reconstructing an image volume directly from cone-beam projection data. Our proposed method addresses this problem by compressing the 3D input into a latent space in a data-driven fashion, and then image reconstruction can be performed in the compressed latent space with a significantly reduced computational cost. To avoid the overfitting problem, the network is first pre-trained using natural images from the ImageNet, and fine-tuned on a publicly available abdominal CT dataset.