## 4 Discussion  
The aim of the presented data on post-SIRT \({}^{166}\)Ho SPECT/CT imaging was to examine the impact of OSEM reconstruction parameters and investigate the advantages of full MC collimator modeling in improving image quality and quantification. The number of iterations used in the OSEM method can impact the qualitative accuracy of the reconstructed images. However, it is important to highlight that as the number of iterations increases, so does the background noise in the images. This may complicate the visual analysis of images, particularly in detecting small lesions. To address this, the study investigated how varying the number of iterations from 1 to 7 while keeping as constant 15 subsets, as well as varying the number of iterations from 1 to 14 with 8 fixed subsets, would affect the Q\({}_{\mathrm{H}}\) and noise values. By analyzing Figure 2A, it is possible to verify that Q\({}_{\mathrm{H}}\) increases with the number of iterations for all spheres (except the smallest one). However, beyond 5 iterations, the contrast coefficients remain relatively constant, showing no significant increase. When evaluating Figure 2B, a distinct behavior is observed. The contrast recovery coefficients increase significantly during the initial iterations and then stabilize for the larger spheres. However, for the smaller spheres, Q\({}_{H}\) continues to increase linearly. It was also evident that the coefficient of variation increased correspondingly with the augmented number of iterations in each reconstruction (Figure 3). This outcome aligns with expectations based on the functioning of the OSEM method, where a higher number of iterations corresponds to increased background noise in the resultant images. As expected, it was also observed in Figure 3 that an increase in the number of subsets corresponded to a rise in CV values for both datasets. By increasing the number of subsets, the projection data are partitioned into smaller segments. This acceleration in segmentation leads to quicker image updates within each iteration, as each subset contributes insights into a smaller region of the image. However, this accelerated updating process can intensify the reconstruction susceptibility to variations present in the input data [18]. Consequently, the reconstructed image can manifest increased noise, ultimately resulting in a heightened CV, as was observed in these datasets. Regarding the quantification of \({}^{166}\)Ho activity, the study successfully determined the CF for each reconstruction. For each subset value investigated, the CF values were closely aligned, providing a measure of confidence in their consistency. However, when full MC collimator modeling was applied, the CF values were slightly lower. This difference is significant and must be considered to ensure the accuracy and reliability of the CF, confirming its suitability for absolute activity quantification in \({}^{166}\)Ho microsphere SPECT/CT imaging. Figure 9: Activity recovery coefficients as a function of the sphere’s volume for the selected reconstructions calculated using both methods (graph **A**—first method and graph **B**—second method). Additionally, Figure 6 also verifies that the activity recovery stabilizes rapidly with fewer iterations. After evaluating image quality and activity quantification, the most suitable OSEM reconstruction parameters were determined based on contrast recovery and activity recovery coefficients. For 15 subsets, 5 iterations provided an optimal balance between noise reduction and accuracy. Increasing iterations to 6 or 7 did not improve contrast but increased noise, making additional iterations unnecessary. Among all reconstructions, 5 iterations with 15 subsets yielded the highest \(Q_{\mathrm{H}}\). Additionally, the reconstruction with 10 iterations and 8 subsets was selected for further analysis, as it achieved high ARC values while maintaining noise levels comparable to the other selected reconstruction.