## 3 Results  
The results in Tables 2-6 provide a detailed quantitative comparison of different low-dose CT (LDCT) denoising models, including the pure vision transformer (PViT) and its variations utilizing attention modules and loss functions. The uncertainty values, provided as standard errors of the mean (SEMs), give us deeper insight into the consistency of these models in producing denoised images. To strengthen the validity of the proposed model and its variations, we also compared their results with the following benchmark models: (i) traditional BM3D [28]; (ii) convolution-based denoising networks, RED-CNN and DRL-E-MP [27]; (iii) hybrid convolution-transformer based model TED-Net [29]; and, finally, (iv) a recent generative denoising network, DSC-GAN [30]. Figures 5 and 6 illustrate visual comparisons and training trends for the piglet dataset. Similarly, Figures 7 and 8 present results for the thoracic dataset, while Figures 9 and 10 correspond to the head dataset. For the abdomen dataset, Figures 11 and 12 provide the respective visual comparisons and training trends. Finally, Figures 13 and 14 showcase the results for the chest dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **NDCT** & **LDCT** & **WL [HU]** & **WW [HU]** \\ \hline Piglet & 300 mAs & 15 mAs & 40 & 400 \\ Thoracic & 480 mAs & 60 mAs & \(-\)650 to \(-\)700 & 1500 to 2000 \\ Abdomen & 502 mAs & 498 mAs & 40 & 40 \\ Chest & 712 mAs & 690 mAs & 40 & 400 \\ Head & 202 mAs & 180 mAs & 30 to 50 & 80 to 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset X-ray current information and Hounsfield unit [HU] windowing for normalization. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **\#Param** & **Training Time** \\ \hline LDCT & 0.6152 \(\pm\) 0.0023 & 25.12 \(\pm\) 0.03 & — & — \\ \hline PViT & 0.9631 \(\pm\) 0.0015 & 43.34 \(\pm\) 0.02 & 1.68 M & 15 min \\ GAM-PViT (DVH) & 0.9730 \(\pm\) 0.0011 & 43.62 \(\pm\) 0.01 & 1.66 M & 15 min \\ LAM-PViT & 0.9622 \(\pm\) 0.0017 & 42.12 \(\pm\) 0.03 & 1.66 M & 15 min \\ \hline GLAM-PViT (MSE) & 0.9728 \(\pm\) 0.0013 & 43.51 \(\pm\) 0.02 & 1.70 M & 18 min \\ GLAM-PViT (SSIM) & 0.9727 \(\pm\) 0.0015 & 43.49 \(\pm\) 0.02 & 1.71 M & 18 min \\ GLAM-PViT (PL) & 0.9732 \(\pm\) 0.0010 & 43.62 \(\pm\) 0.01 & 1.75 M & 20 min \\ \hline GLAM-PViT (proposed) & 0.9756 \(\pm\) 0.0010 & 43.84 \(\pm\) 0.01 & 1.82 M & 30 min \\ \hline Benchmark 1: RED-CNN & 0.9321 \(\pm\) 0.0021 & 39.68 \(\pm\) 0.04 & 3.33 M & 50 min \\ Benchmark 2: TED-Net & 0.9536 \(\pm\) 0.0019 & 43.15 \(\pm\) 0.03 & 3.28 M & 80 min \\ Benchmark 3: DSC-GAN & 0.9602 \(\pm\) 0.0018 & 42.23 \(\pm\) 0.03 & 4.16 M & 120 min \\ Benchmark 4: BM3D * & 0.8263 \(\pm\) 0.0027 & 36.55 \(\pm\) 0.04 & n/a & 60 min \\ Benchmark 5: DRLEMP & 0.9529 \(\pm\) 0.0018 & 41.66 \(\pm\) 0.03 & 2.80 M & 60 min \\ \hline \hline \end{tabular} \(\dagger\) Higher SSIM/PSNR values indicates more accurate denoised image. * BM3D involves algorithmic parameters and settings (e.g., block size, search window size, thresholding parameters) that can be tuned, but these are not using standard error of the mean (SEM).  
\end{table} 
Table 2: **Piglet Dataset**. Average SSIM and PSNR with No. of parameters using two transformer blocks for the PViT model variants (GAM: gradient attention module, LAM: Laplacian attention module, GLAM: gradient–Laplacian attention module) and variants of the loss functions (MSE, SSIM, PL, proposed: MSE + SSIM + PL) used for the training (150 epochs). Uncertainties were calculated using SEM.