###### Abstract  
X-ray computed tomography (CT) is vital for medical diagnostics, but frequent radiation exposure raises concerns, driving the adoption of low-dose CT (LDCT) to mitigate risks. However, LDCT often introduces noise, compromising diagnostic accuracy. This paper proposes a pure vision transformer (PViT) for LDCT denoising, enhanced with a gradient-Laplacian attention module (GLAM) to improve edge preservation and fine structural detail reconstruction. The model's robustness was validated across five diverse datasets (piglet, head, abdomen, chest, thoracic), demonstrating consistent performance in preserving anatomical structures. Extensive ablation studies on attention configurations and loss functions further substantiated the contributions of each module. Quantitative evaluation using PSNR and SSIM, alongside radiologist assessment, confirmed significant noise suppression and sharper anatomical boundaries, particularly in regions with fine details such as organ interfaces and bone structures. Additionally, in benchmark comparisons against state-of-the-art LDCT models (RED-CNN, TED-Net, DSC-GAN, DRL-EMP) and traditional methods (BM3D), the model exhibited lower parameter and stable training performance. These findings highlight the model's robustness, efficiency, and clinical applicability, making it a promising solution for improving LDCT image quality while maintaining computational efficiency. gradients-Laplacian attention modules; low-dose CT denoising; deep learning; medical image processing; machine learning; vision transformers +
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details, which limits its suitability for highly detailed LDCT images [4]. Recent advancements like adaptive filtering [4; 5] adjust filter strength based on image regions, improving edge preservation and reducing oversmoothing. However, adaptive filters may still struggle in regions with high noise levels, where detail is lost due to excessive smoothing in non-homogeneous areas. Approaches like non-local means (NLMs) [6; 7], anisotropic diffusion [8], and total variation (TV) minimization [4] are designed to maintain image structure while reducing noise. NLM denoising, for example, compares and averages similar pixel patches across the image, as demonstrated by [PERSON] et al. [9], who modified NLMs to improve denoising in LDCT images. While effective, this approach is computationally intensive, limiting its efficiency in real-time applications, especially for large datasets. Anisotropic diffusion, another model-based technique, diffuses noise differently across regions to retain edges while smoothing uniform areas [10], but it can amplify artifacts in areas with low contrast, impacting diagnostic accuracy [8]. TV minimization, a popular choice for iterative reconstruction, reduces noise by minimizing image gradients [11]. [PERSON] et al. [12] presented a TV-based method optimized for LDCT imaging, which retains edges well; however, this method can lead to staircase artifacts, particularly in areas with smooth gradients. Transform-domain methods decompose the image into wavelet or frequency components, making it easier to filter out noise selectively [13]. Wavelet transform denoising, for instance, decomposes an image into wavelet coefficients and selectively thresholds these coefficients to remove noise. [PERSON] et al. [14] improved on traditional wavelet methods with a multi-scale adaptive thresholding approach, which enhances detail preservation by handling noise at various frequencies. Despite their benefits, wavelet-based methods often require tuning across multiple scales, making them computationally complex and difficult to adapt for heterogeneous noise patterns, a limitation highlighted in [PERSON] et al. 's work. Fourier transform denoising, which uses frequency-based filtering, is also widely applied but can inadvertently blur details by removing high-frequency noise components indiscriminately, reducing diagnostic value in areas with high texture or detail [15].