Abstract: Sparse CT reconstruction can reduce patient radiation dose and is of great significance for clinical diagnosis. In deep learning-based image reconstruction tasks, classic networks such as Uformer, Restormer, and AST fail to consider multi-scale and directional information in images, neglecting the balance between local details and global structure, resulting in limited artifact suppression. To address these issues, a Multi-scale Attention Adaptive Fusion Transformer (MAAF-Transformer) network was proposed. A parallel attention fusion strategy was adopted by this network, which was combined with a multi-scale channel-directional awareness module to capture artifact features. The weights were dynamically adjusted by utilizing an adaptive spatial channel attention module. Finally, effective information was filtered through a gated deep convolutional feedforward network. Experimental results show that, under 60 sparse views, MAAF-Transformer achieves 0.7141 dB higher peak signal-to-noise ratio (PSNR), 0.33%higher structural similarity (SSIM), and 7.76% lower root mean square error (RMSE) than classic Uformer, demonstrating excellent performance in terms of visual effects as well. Evidently, MAAF-Transformer possesses higher sparse reconstruction accuracy and stronger artifact suppression capabilities.