# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 4 Discussion
In addition, our study aimed to identify general relationships between patient characteristics and radiation dose, as well as the effect of isocenter location and tube parameters on radiation exposure [18]. In the context of our analysis, an interesting comparison is the study by [PERSON] et al. that evaluated radiation doses in high-resolution computed tomography (HRCT) of the chest with patient size-specific dose estimates (SSDEs). Their retrospective analysis involving nearly 2000 patients showed a significant positive correlation between BMI and all key dose parameters, such as CTDIvol, DLP, and SSDEs. These findings are consistent with our observations of the relationship between BMI, body dimensions, and total DLP and delayed-phase DLP for the liver. Importantly, the authors emphasize that the use of SSDEs allows for more accurate dose matching to patient morphology than traditional metrics. Our study also shows the effect of patient size on exposure parameters; however, it focuses on the abdominal and pelvic region, while [PERSON] analyzed the thorax. Despite the anatomical differences, both studies point to the need to personalize imaging protocols according to patient characteristics [19]. An additional relevant comparison is the study by [PERSON] et al., which evaluated the effect of patient positioning accuracy on radiation dose using a novel 3D camera-based system in CT imaging. The authors demonstrated that precise patient centering significantly reduces radiation dose, particularly in the chest, abdomen, and pelvis, by minimizing the degradation of automatic exposure control and bowtie filter performance. These findings partially contrast with our results, which did not show a statistically significant dose difference based on isocenter positioning [20]. The collected results indicate that patient body size, particularly BMI and maximum lateral dimension, has a significant impact on total radiation dose, emphasizing the need for personalized imaging protocols. These correlations were especially evident in scans encompassing both the abdomen and pelvis. For abdominal-only examinations, a weaker correlation between body size and exposure parameters (mA) was observed, possibly reflecting the effectiveness of automatic exposure control systems in compensating for anatomical differences. In our study, patient positioning at the isocenter did not show a statistically significant impact on radiation dose; however, this may be attributed to the binary classification method we applied and the broader scan range. In contrast, [PERSON] et al., who focused on head CT, demonstrated clear dose differences when the phantom was positioned off-center, suggesting that isocenter accuracy may be more critical in small anatomical areas [17]. Similarly, [PERSON] et al. 's study on liver biopsies showed increased radiation exposure for deep lesions and additional helical scans, consistent with our findings on body size influence, though in a different clinical context (interventional vs. diagnostic procedures) [18]. The study by [PERSON] et al. found a strong correlation between BMI and all major dose metrics (CTDIvol, DLP, SSDEs) in chest CT, highlighting the advantages of SSDEs over traditional metrics for accurately adjusting dose to patient morphology. While their study focused on a different anatomical region, the observations align with ours. Interestingly, [PERSON] et al. also observed a negative correlation between scan length and CTDIvol/SSDEs, likely due to automatic exposure control systems, a relationship not observed in our data, possibly due to protocol differences [19]. Moreover, [PERSON] et al. demonstrated that precise patient centering using a 3D camera system significantly reduced radiation dose in the chest, abdomen, and pelvis, partially contrasting with our findings. These discrepancies may result from methodological differences: Their study applied a continuous spatial evaluation, while ours used a binary classification.