A novel WF-Mamba algorithm for eliminating radial edge artifacts in CT images reconstruction

To address the severe radial-edge artifacts and the loss of feature details in CT image reconstruction, this paper proposes a novel WF-Mamba algorithm integrating the Adaptive Wavelet Frequency Interaction Network (AW-net) with the Restructured Mamba Adjustment Module (RMAM). The AW-net leverages frequency-domain properties to initially enhance high-frequency components corresponding to edges and fine details in CT images. By integrating the Transformer architecture with the learnable 2D discrete wavelet transform (2D-DWT), we design the Optimized Encoding Block (OEB) and Wavelet Decoding Block (WDB) to improve the representation of image details and textures. Additionally, the Wavelet Aggregation Unit (WAU) is proposed to further enhance reconstruction quality by facilitating effective multi-scale information interaction. The RMAM incorporates a six-directional scanning mechanism to refine the initial reconstruction results generated by the AW-net, thereby enhancing the capture capability of fine-grained information and reducing computational complexity. To optimize model performance, a hybrid objective loss function is established by combining the L2 loss with Self-Supervised Wavelet Loss (SSWL). Experimental results demonstrate that the WF-Mamba achieves superior performance in eliminating radial edge artifacts, restoring textural details, and preserving boundary features compared with other discussed methods. Quantitatively, the WF-Mamba achieves a PSNR of 31.85 dB, an SSIM of 0.948, and a normalized MSE of 6.53 × 10^-4 on the actual projection dataset, outperforming all compared methods. Furthermore, the reconstructed marked contour curves more closely approximate the reference image, demonstrating highly accurate restoration of the raw detailed structure.