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A study was conducted on denoising low-dose CT images, analyzing the issues of low computational performance and insufficient generalization in neural network denoising for artifact suppression. An Anisotropic Total Variation Deep Unfolding Denoising Network was adopted, with the new method incorporating the edge characteristics of adjacent voxels by introducing an anisotropic TV regularization term to preserve the structural information of images and avoid edge blurring caused by isotropic TV. The Chambolle-Pock algorithm was employed to solve the mathematical model, adapting it for deep unfolding into convolutional neural networks. Additionally, a pixel attention mechanism was integrated for network optimization to capture important image details. Experimental tests based on the Mayo 2016 dataset demonstrated that this method outperforms traditional methods and other advanced network models in image denoising, showing superior performance in PSNR, SSIM, and VIF metrics. This method meets the requirements for high-quality reconstruction of low-dose CT images.