Sliding volume-based streak artifact reduction network (S-STAR Net) for ultra-sparse-view computed tomography

Cone-beam computed tomography (CBCT) is a widely used imaging technique. In practical applications, reducing projection views can decrease radiation exposure and accelerate scanning speed, with potential benefits for stationary CT systems. However, ultra-sparse-view acquisition (e.g., ≤ 30 views) introduces severe streak artifacts that degrade image quality. This poses a critical challenge because it is difficult to differentiate artifacts from real structures. We propose a sliding volume-based streak artifact reduction network (S-STAR Net) to remove artifacts while preserving structural details. Our method introduces three key technical innovations: (1) A sliding sampling sub-volume approach to process 3D sub-volumes, fully leveraging spatial context. (2) A difference enhancement (DE) loss to help separate artifacts from real structures. (3) Novel network includes volume-attention aided residual (VAR) blocks and Fourier transform convolution (FTC) blocks for multi-domain feature learning. Evaluated under 30 projection views, the method was tested on two datasets: a walnut dataset and the CQ500 head CT dataset. Quantitative metrics (PSNR/SSIM) and qualitative assessments (multi-planar visualization, residual error maps) demonstrate that S-STAR Net achieves superior performance in both artifacts suppression and detail preservation compared to existing approaches. The proposed method effectively addresses streak artifacts and recovers subtle structures in ultra-sparse-view CBCT reconstruction. Its robustness suggests broad applicability for medical image denoising, artifact reduction, and 3D image enhancement tasks.