Shaoyong Hong1, Bo Yang2, Yan Chen2, Hao Quan3, Shan Liu4, Minyi Tang5,*, Jiawei Tian6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1091-1112, 2025, DOI:10.32604/cmes.2025.066165
- 31 July 2025
Abstract 3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and… More >