## 4 Discussion
Different size of the detector and slice collimation setting in P7 resulted in declining organ-absorbed dose in pediatric CT CAP analysis by contrasting the organ dose in all protocols. Besides dose reduction, it also indicates that there is no significant difference in image quality. According to the previous study reports, apart from growing pitch factor and reducing tube current and tube voltage in the optimization phase, increasing slice collimation is a technique to minimize radiation dose without sacrificing image quality performance [41; 42]. Furthermore, previous research indicates that increased beam collimation resulted in a decrease of around 17% over-ranging in CT study, which may prevent overexposure to patients and decreased dose to radiosensitive organs beyond primary beam [43]. However, a wider detector z-coverage screening applied may increase radiation scattering. Scattered radiation can cause hypodense artefacts, affect the stability of the CT number, and the scatter-induced noise in the images may decrease the CNR [44]. The radiation dose metrics in terms of CTDIvol, DLP, E and organ-absorbed dose results were substantially higher in P9. The body habitus of children populations varied in size and weight. In addition to the patient's size, the patient's anatomy has a major effect on X-ray attenuation and noise. At most children's health centres, CT CAP was applied for further investigation to determine the metastasis of cancer to the patients who were already diagnosed with primary cancer diseases. With the introduction of automatic tube current modulation (ATCM), this technique was used to optimize the radiation dose, particularly amongst pediatric patients. Moreover, with using TCM, 80% of organ-absorbed dose was reduced in pediatric CT CAP examination. This study was similar to the previous study shows by using TCM, 68% of effective dose reduce in CT CAP examination [40]. Therefore, the patient size, the shape, and anatomy of the patient has a significantly effect on TCM in terms of declining radiation dose without sacrificing image quality [4; 24]. The adjustments to these scanning parameters were investigated as to how these influences the patient dosage and the performance of CNR, SNR, and image noise. Nevertheless, there is a trade-off between image quality and dosage. The larger dose leading to the higher signal improves contrast resolution, lowering the noise and providing clearer depiction of low-contrast structures. Detection of low contrast information and lesions is restricted primarily by noise, which can be minimized by increasing the radiation exposure. Dose output of CT scanners operating in such modes can be described along with standard-dose indicators by image quality values. Hence, the trade-off between image quality and radiation dose were accessed by FOM based on SNR and CTDI\({}_{\text{vol}}\) in this study. Several studies explored the probability of dose reduction by optimization dependent on FOM approach [14; 45; 46]. In addition, artificial intelligence has lately been commonly used to optimize radiation-based processes and has several benefits over conventional approaches. The usage of artificial intelligence contributes to refining photon radiation-based applications in both the medical and manufacturing industries [47; 48; 49]. Likewise, Machine Learning and Deep Learning a subset of artificial intelligence have been used in a number of applications to evaluate complicated data sets and to identify similarities and associations within those data without being directly configured [50; 51]. This research has some limitations to list. First, this analysis just checked the alteration of the pediatric CT CAP protocols on a one-year-old anthropomorphic phantom. Therefore, the age variation in anthropomorphic phantom can be used to present radiation intensity and the dosage received by the organs of children owing to the variation of body size and habitus. Second, this research did not conduct a subjective evaluation of the accuracy of the picture by an expert radiologist.