Variable Step Normalized Least Mean Square Guided by Composite Desired Signal for Few-View Computed Tomography Denoising

Background: Low-dose CT provides essential diagnostic information while minimizing radiation exposure through few-view reconstruction techniques. However, these techniques often introduce noise and artifacts, affecting diagnostic accuracy. Although $L_0$ -smoothing regularization methods partially address these issues, their fixed sparsity constraint cannot adapt to CT image complex characteristics, and they remain highly sensitive to regularization parameter selection. Objective: To propose a novel CT image denoising method named Variable Step Normalized Least Mean Square $L_0$ -smoothing (VSNLMS-$L_0$ ) that achieves an optimal balance between noise reduction and structural preservation while reducing sensitivity to regularization parameter selection. Methods: The VSNLMS-$L_0$ method employs an adaptive framework that dynamically responds to local image characteristics. The variable step-size strategy enables precise calibration of processing intensity across regions with varying noise levels and detail complexity, ingeniously combining filtered back projection (FBP) reconstruction results with $L_0$ -smoothing to create a composite desired signal. Conclusions: This approach offers an effective solution for enhancing low-dose CT image quality and improving diagnostic reliability.