Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction

2017/03/04 by Jawook Gu, Jong Chul Ye, Gu, Jawook +1
Earth and Planetary Sciences · Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Imaging Techniques and Applications #Seismic Imaging and Inversion Techniques

paper · pdf · doi:10.48550/arxiv.1703.01382

openalex publication_date 2017/03/04 · openalex created_date 2017/03/16 · openalex updated_date 2026/07/28

Abstract

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.

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