Low-Dose CT 3D Reconstruction Using Convolutional Sparse Coding and Gradient L0-Norm

The potential radiation damage in CT scans have been receiving increasing attention. However, reducing the scan dose will degrade the image quality and affect the diagnosis results. Aiming at addressing the above problems, a three-dimensional (3D) reconstruction algorithm combining convolutional sparse coding and gradient L-0-norm is proposed herein. The proposed algorithm uses the frequency decomposition reconstruction form to perform unsupervised multiscale online convolution sparse-coding constraints on high-frequency components, and gradient L-0-norm constraints on low-frequency components to achieve the suppression and organization of noise artifacts in low-dose CT imaging keep the details. Moreover, three different scales of 3D filter sets are used in convolutional sparse coding, which can effectively adapt to the feature information at different scales and improve the coding ability. The experimental results of abdominal CT simulation data and real-time scan data show that the proposed algorithm can obtain fewer noise artifacts, high contrast in structural details, and better imaging results in the reconstruction process of 25% conventional dose.