Brain computed tomography (CT) is a diagnostic imaging tool routinely used to assess all paediatric neurologic disorders and other head injuries. Despite the continuous development of paediatric CT imaging, radiation exposure remains a concern. Using diagnostic reference levels (DRLs) helps to manage the radiation dose delivered to patients, allowing one to identify an unusually high dose. In this paper, we propose DRLs for paediatric brain CT examinations in Saudi clinical practices and compare the findings with those of other reported DRL studies. Data including patient and scanning protocols were collected retrospectively from three medical cities for a total of 225 paediatric patients. DRLs were derived for four different age groupings. The resulting DRL values for the dose–length product (DLP) for the age groups of newborns (0–1 year), 1-y-old (1–5 years), 5-y-old (5–10 years) and 10-y-old (10–15 years) were 404 mGy cm, 560 mGy cm, 548 mGy cm, and 742 mGy cm, respectively. The DRLs for paediatric brain CT imaging are comparable to or slightly lower than other DRLs due to the current use of dose optimisation strategies. This study emphasises the need for an international standardisation for the use of weight group categories in DRL establishment for paediatric care in order to provide a more comparable measurement of dose quantities across different hospitals globally.

Objectives: Diagnostic reference levels (DRLs) are crucial tools for optimizing radiation exposure during different radiological examinations. This study aimed to establish preliminary DRLs for commonly performed computed tomographic angiography (CTA) examinations in Saudi Arabia. Methods: Data for three types of CTA examinations (cerebral, pulmonary, and lower-extremity) were collected from six medical cities across Saudi Arabia. Data sets related to 723 CTAs with a mean patient weight of 75 kg were analysed in detail. The DRL values were determined based on the 75th, median, and 25th CT dose index volume (CTDI_vol) and dose length product (DLP) values. Results: The established DRLs were 1221 mGy cm for cerebral CTAs, 475 mGy cm for pulmonary CTAs, and 1040 mGy cm for lower-extremity CTAs. These values were comparable to those reported in other studies. Conclusions: This study provides preliminary DRLs for three common CTA procedures in Saudi Arabia. The widespread implementation of a low kVp and a high level of image reconstruction (IR) presents an opportunity for further dose reduction. These findings can serve as a foundation for future nationwide DRL surveys and the optimization of CTA imaging protocols in Saudi Arabia.

Background/Objectives: Digital mammography is widely used for breast cancer screening; however, variations in system design and automatic exposure control (AEC) strategies can lead to significant differences in radiation dose, potentially affecting the diagnostic quality and patient safety. In this study, we aimed to determine the effect of various mammographic technologies on the in vivo mean glandular doses (MGDs) that are received in clinical settings. Methods: The MGDs and applied acquisition parameters from 194,608 mammograms, acquired employing AEC using different digital mammography systems (GE, Siemens, and two different models of Hologic), were retrospectively collected. The potential variation in MGD resulting from different technologies (system and target/filter combination) was assessed employing the Kruskal–Wallis test, followed by Dunn’s post hoc. The AEC optimization of acquisition parameters (kVp, mAs) within each system was investigated through a multi-regression analysis as a function of the compressed breast thickness (CBT). The trend line of these parameters in addition to the MGD and source-to-breast distance were also plotted and compared. Results: There were significant variations in delivered doses per CBT based on which technology was used (p < 0.001). The regression analyses revealed system-specific differences in AEC adjustments of mAs and kVp in response to CBT changes. As the CBT increases, the MGD increases with different degrees, rates, and patterns across systems due to differences in AEC strategies. Conclusions: The MGD is affected by the applied technology, which is different between systems. Clinicians need to be aware of these variations and how they affect the MGD. Additionally, manufacturers may need to consider standardizing the implemented technology effects on the MGDs.