## 3 Results  
Figure 2 shows the Q\(  
Figure 4 demonstrates visually the impact of the number of iterations for both fixed subset values. Both rows display very similar images, as expected, given that the number of iterations in the second row is doubled while the number of subsets is nearly halved compared to the  
Figure 4: Phantom’s reconstructions: 1, 3, and 7 iterations with 15 subsets (**top** row) and 2, 6, and 14 iterations with 8 subsets (**bottom** row). Figure 3: Coefficient of variation (%) as a function of the number of iterations for both fixed subset values. Figure 2: Relationship between the contrast recovery coefficients and the number of iterations for all six spheres. Graph (**A**) shows data for a fixed value of 15 subsets, while Graph (**B**) shows data for a fixed value of 8 subsets. top row. As anticipated, an increasing number of iterations results in images with a higher contrast and increased noise. In terms of activity quantification, the CF and its associated uncertainty are shown for each reconstruction in Figure 5, including reconstructions with the same parameters and full MC collimator modeling. Due to the extensive computation time required (which was not feasible with the GPU) and the predictable nature of the results, CF values were only obtained for 1 and 10 iterations, with a fixed number of 8 subsets. The CFs remain relatively constant with the number of iterations for each subset configuration, with slightly lower values observed for 8 subsets (mean: \(14.6\pm 0.5\) cps/MBq) compared to 15 subsets (mean: \(14.9\pm 0.5\) cps/MBq). Additionally, it was observed that applying full MC collimator modeling reduced the CF to values ranging between 10.9 and 12.1 cps/MBq. The ARCs were determined using two distinct methods. Figure 6 presents the ARC as a function of the number of iterations, calculated using the first method. By observing Figure 6, it can be said that the ARCs remain practically constant for all spheres after 5 iterations for 15 subsets (graph A) and 10 iterations for 8 subsets (graph B). After carefully analyzing the contrast, noise, and activity recovery results for all reconstructions, two different reconstructions were selected for further investigation to assess the impact of full MC collimator modeling on the results. The selected reconstructions were as follows: 5 iterations with 15 subsets and 10 iterations with 8 subsets. Figure 7 visually demonstrates the impact of full MC collimator modeling, along with a profile of the line present in the images, as the differences in the images are not perceptible to the eye. Figure 8 presents the contrast recovery coefficients and the contrast-to-noise ratio for the final four selected reconstructions. Graph A shows that for the three smaller spheres, the contrast is very similar across all four reconstructions. However, for the three larger spheres, the reconstruction with 10 iterations, 8 subsets, and full MC collimator modeling provides the highest contrast. Graph B shows that the CNR was higher for the reconstruction with 5 iterations, 15 subsets, and full MC collimator modeling. It is also important to note that none of the reconstructions provided a CNR greater than 5 for the smaller sphere, meaning that this sphere was not detectable in any of the images. Figure 5: CF (cps/MBq) as a function of the number of iterations for both fixed subset values and including full MC collimator modeling. As a final result, Figure 9 displays the ARCs as a function of the sphere's volume for both methods (Equations (5) and (6)). For both graphs, Equation (7) was fitted to the data points, and the determination coefficients (R\({}^{2}\)) are shown. Graph A clearly demonstrates that the reconstruction with 10 iterations, 8 subsets, and full MC collimator modeling provides the highest activity recovery. However, Graph B, which uses the EANM guidelines to calculate the ARC, shows that, regardless of the reconstruction, the ARCs are similar across all reconstructions.