###### Abstract
Statistical denoising relies on probabilistic models to distinguish between noise and useful signals. Techniques such as K-SVD (sparse representation) utilize dictionary learning to construct a sparse, noise-free representation of the image [16]. The K-SVD method is adaptable and effective for LDCT, particularly in low-contrast areas; however, it can fail in high-noise or high-contrast regions where the learned basis functions may inadequately represent essential details. Bayesian estimation models, including maximum a posteriori (MAP) estimations, apply statistical priors to separate noise from signal [17]. While MAP estimation can yield high-quality denoising results, it relies heavily on accurate prior models, and incorrect priors can lead to oversmoothing, reducing the visibility of small or subtle structures. These studies reveal that while traditional denoising methods offer significant noise reduction capabilities for LDCT, each has inherent limitations. Recent model improvements attempt to address these issues, but achieving a perfect balance among noise reduction, computational efficiency, and detail retention remains an ongoing challenge. Recently, deep learning models have emerged as powerful tools in medical image processing. Vision transformers (ViTs) offer a promising alternative to convolution-based networks by leveraging self-attention mechanisms to capture the long-range dependencies in images [18]. This study serves as the continuation of the paper by the authors, with the use of a pure vision transformer (PViT)-based architecture [19]. However, directly applying ViTs to LDCT denoising introduces challenges in accurately retaining fine structural details and edges, as shown in Figure 1. Comparing Figure 1b with the denoised image in Figure 1c using PViT alone, it is evident that the anatomical details within the highlighted blue region of interest are not well preserved. This discrepancy arises primarily from PViT's limited ability to enhance fine structural details and detect subtle edge variations in low-dose CT images. Therefore, this paper primarily addresses the mentioned limitation by implementing an edge detection attention module. The used gradient and Laplacian filters, integrated into an attention module, are designed to enhance edge information by capturing both first- and second-order derivatives, detecting changes in the boundaries and textural details of the image, thus improving the representation of critical anatomical boundaries in the CT images. By combining attention mechanisms with these edge detectors, the PViT model aims to deliver high-quality, denoised LDCT images with greater edge consistency. The contributions of this paper are as follows:  
1. There have been few transformer-based studies for LDCT denoising, so we aimed to address this gap by introducing a novel pure vision transformer with an edge detection attention module. 2. We leveraged the concept of edge detection attention modules in vision transformers that target the preservation of CT image edges and textural details. 3. We investigated the effectiveness of each edge detection attention module in LDCT denoising by performing ablation experiments using various kernels, datasets, and objective functions. 4. Benchmark testing with state-of-the-art LDCT denoising models was also performed to compare the proposed model with the recent developments in the LDCT denoising task in the research community.