## 4 Discussion
The relationship between patient head size, as a function of the Dw and the SSDE, demonstrated a strong negative correlation in the younger age groups, with R-squared values of approximately 0.99 (\(p<0.001\)). In the 12-to-15-year age group, a moderate correlation was observed, with an R-squared value of 0.64 (\(p<0.001\)). In contrast, the relationship between the Dw and the CTDI\({}_{\rm vol}\) appeared negligible, as CTDI\({}_{\rm vol}\) values remained constant within the same age groups, indicating that the Dw and the CTDI\({}_{\rm vol}\) are nearly independent, with the CTDI\({}_{\rm vol}\) not varying sufficiently to reflect differences in patient head size. [PERSON] et al. [22] estimated absorbed doses to the brain using data from hybrid computational human phantoms and Monte Carlo radiation transport techniques; these were found to range from 28 to 43 mGy in pediatric patients aged 0 to 15 years. In our study, using Monte Carlo simulations based on stylized computational phantoms, we found that the brain dose in pediatric patients ranged from 21 to 39.5 mGy. Table 2 shows that organ doses to the brain and eye lenses increase with age due to higher radiation exposure. The median (IQR) organ dose to the eye lens in the 12-to-15-year age group was the highest at 50.1 (49.9, 52) mGy. The eye lens is highly radiosensitive, and radiation exposure from a CT brain scan poses risks such as cancer and cataracts [23]. Thus, careful scan positioning to avoid radiation exposure to the eye lens is essential. Proper patient positioning, such as aligning scans along the supraorbital meat line, can help minimize radiation exposure to the lens [24,25]. Although a single brain CT scan may not pose an immediate cancer risk, pediatric patients may undergo multiple scans, increasing the cumulative absorbed dose and the potential for developing cancer later in life [6,25]. Figure 2 shows that the median mean CT number in the ROI (HU) for the 6-to-12-year age group was lower than that for the 6-month-to-3-year and 3-to-6-year age groups. This is primarily due to a higher incidence of hypodense lesions and hydrocephalus in pediatric patients aged 6 to 12 years, which contributes to lower mean CT numbers. Notably, there were outliers in the 6-to-12-year and 12-to-15-year age groups, with mean CT numbers of 50 HU and 43 HU, respectively, which were attributed to specific clinical conditions: bilateral subdural fluid collection and pneumocphalus in the former case, and multiple pneumocphalus and moderate hydrocephalus in the frontal lobe in the latter. Head size as a function of the Dw increased with age (Figure 3), consistent with [PERSON] et al. 's [18] investigation of the SSDE using pediatric helical head CT protocols. The correlation between the conversion factor and age indicates a strong negative relationship, with a correlation coefficient of r = \(-\)0.70, indicating that the conversion factor decreases with age. This is consistent with [PERSON] et al. 's [17] findings. Their study reported that the relationship between the SSDE and age and weight in pediatric brain CT scans, using the AEC system, were best fit by applying a power function, resulting in mean estimation errors close to 0, with R-square values of 0.71 and 0.86, respectively. In our study, the evaluation metrics, R-squared and RSE, improved when age and weight were applied to the multivariate linear regression model (Table 3). Given the complexity of estimating the SSDED\({}_{\rm Dw}\), a mathematical model can be used to predict the SSDED\({}_{\rm Dw}\). The equation for the multiple linear regression model is SSDED\({}_{\rm Dw}\) = 18.64 + 1.34(age)0.04(weight). This model, with an R-squared value of 0.87 and an RSE of 2.26, indicates that its predictions closely approximate the true values, demonstrating high predictive accuracy.