Dual-domain projection fidelity network for sparse-view helical CT reconstruction

Abstract

Sparse-view computed tomographic (CT) image reconstruction aims to shorten scanning time, reduce radiation dose, and yield high-quality CT images simultaneously. Some researchers have developed deep learning (DL) based models for sparse-view CT reconstruction on the circular scanning trajectories. However, cone beam CT (CBCT) image reconstruction based on the circular trajectory is theoretically an ill-posed problem and cannot accurately reconstruct 3D CT images, while CBCT reconstruction of helical trajectory has the possibility of accurate reconstruction because it satisfies the tuy condition. Therefore, we propose a dual-domain helical projection-fidelity network (DHPF-Net) for sparse-view helical CT (SHCT) reconstruction. The DHPF-Net mainly consists of three modules, namely artifact reduction network (ARN), helical projection fidelity (HPF), and union restoration network (URN). Specifically, the ARN reconstructs high-quality CT images by suppressing the noise artifacts of sparse-view images. The HPF module uses the measured sparse-view projection to replace the projection values of the corresponding position in the projection of the ARN, which can ensure data fidelity of the final predicted projection and preserve the sharpness of the reconstructed CT images. The URN further improves the reconstruction performance by combining the sparse-view images, IRN images, and HPF images. In addition, in order to extract the structure information of adjacent images, leverage the structural self-similarity information, and avoid the expensive computational cost, we convert 3D volumn CT image into channel directions. The experimental results on the public dataset demonstrated that the proposed method can achieve a superior performance for sparse-view helical CT image reconstruction.