Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
This addresses image quality issues in clinical applications like C-arm CT, but appears incremental as it builds on existing methods with a novel architecture.
The paper tackles the problem of heavy artifacts in limited-angle CT reconstruction by proposing a multi-scale wavelet domain residual learning architecture, which effectively eliminates artifacts and preserves edge and global structures.
Limited-angle computed tomography (CT) is often used in clinical applications such as C-arm CT for interventional imaging. However, CT images from limited angles suffers from heavy artifacts due to incomplete projection data. Existing iterative methods require extensive calculations but can not deliver satisfactory results. Based on the observation that the artifacts from limited angles have some directional property and are globally distributed, we propose a novel multi-scale wavelet domain residual learning architecture, which compensates for the artifacts. Experiments have shown that the proposed method effectively eliminates artifacts, thereby preserving edge and global structures of the image.