Background: Computed Tomography (CT) is crucial in medical diagnosis, particularly for head examinations. Diagnostic Reference Levels (DRLs) are pivotal in balancing diagnostic efficacy with radiation safety. International organizations such as the International Atomic Energy Agency (IAEA) and the International Commission on Radiological Protection (ICRP) provide guidelines for establishing DRLs, emphasizing their importance in optimizing radiation doses. Aim: This study aimed to establish and standardize DRLs for head CT imaging in institutional and regional settings, emphasizing the need for tailored reference levels specific to each region’s practices and standards. Method: Data collection included patient demographics, imaging parameters, and radiation dose indices, namely volume-weighted CT dose index (CTDIvol) and dose-length product (DLP). Statistical analyses were conducted to determine the median and 3rd quartile values for establishing DRLs. The results were compared with national and international benchmarks to assess variations in radiation doses across regions. Results: Demographic profiles detailed gender distributions and ages across hospitals. Imaging parameters exhibited variability in tube voltage, milliampere-seconds (mAs), pitch, scan length, and field of view (FOV). For CTDIvol, the 3rd quartile value was 65.8 mGy (range: 24.8–85.9 mGy), and for DLP, it was 1230.95 mGy·cm (range: 382.3–1189.0 mGy·cm). These values were slightly higher than the national DRLs for Saudi Arabia in 2021 and other international benchmarks, underscoring the need for further optimization and alignment of protocols. Conclusion: Optimizing and standardizing DRLs for head CT imaging is crucial for effectively managing radiation doses while ensuring diagnostic accuracy. Comparison with national and international benchmarks highlighted the importance of tailoring reference levels to regional practices and standards, ensuring patient safety without compromising diagnostic efficacy.

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.

Background: Breast density is increasingly recognized as a vital risk factor that affects early breast cancer detection. Therefore, this study was aimed at evaluating the agreement between different breast density measurements across multiple imaging modalities and identifying the best breast cancer predictor among these methods. Methods: Data for women over 30 years old who underwent mammography, synthetic mammography of digital breast tomosynthesis (SM‐DBT), ultrasound (US), and magnetic resonance imaging (MRI) were collected. Breast density was assessed by two breast radiologists using the American College of Radiology (ACR) categories and the visual analog scale (VAS) for percentage density (%PD). The agreement was calculated using the kappa coefficient ( k ) and Spearman correlation coefficient ( ρ ). Logistic regression odds ratios (ORs) were used to assess the best predictor of breast cancer based on breast density. Results: Among 77 women (mean age 47.34 years), 25 had breast cancer. Categorical breast density assessments showed the highest agreement between mammography and SM‐DBT ( k = 0.535) and moderate agreement between mammography and MRI ( k = 0.452). VAS analysis revealed moderate positive correlations between mammography and SM‐DBT ( ρ = 0.49), mammography and MRI ( ρ = 0.56), and SM‐DBT and MRI ( ρ = 0.56), p < 0.05. Ultrasound showed the lowest correlation with all breast imaging modalities. Breast cancer risk prediction based on breast density showed significant associations for mammography (OR = 3.09) and MRI (OR = 4.16), p < 0.05. Conclusion: The results suggest notable variability in radiologists′ breast density assessment across different imaging modalities. MRI showed a greater ability to identify dense breast tissue and demonstrated potential value in breast cancer risk prediction, although these findings should be interpreted cautiously given the limited sample size. Establishing standardized approaches to breast density assessment remains important to improve the accuracy of breast cancer screening and risk prediction, and further research with larger cohorts is warranted.

Sprouting boosts bioactive compounds in Brassicaceae, including glucosinolates, amino acids, and anthocyanins. This study examined gamma radiation seed priming effects on growth, metabolism, and bioactive accumulation in Nasturtium officinale, Eruca sativa, and Raphanus raphanistrum sprouts. Seeds of the three species were primed with gamma radiation (0.1 kGy/h) and grown under controlled conditions. Biomass was measured; metabolic profiling quantified glucosinolates, amino acids, flavonoids, and anthocyanins. Enzymatic activities for glucosinolate and flavonoid biosynthesis were assessed. Antioxidant potential was assessed using FRAP and DPPH assays; antimicrobial activity was tested against Escherichia coli and Enterococcus faecalis. Microbial counts (APCs and Coliforms) were measured. Gamma radiation increased biomass by 48%, 36%, and 71% in N. officinale, E. sativa, and R. raphanistrum, respectively. Glucosinolates rose, especially glucoerucin (up to 227% in E. sativa) and glucoraphenin (up to 60%), linked to higher precursor amino acids (leucine, tryptophan, methionine), glutathione, and activities of glutathione S-transferase and sulfotransferase. Myrosinase activity increased, boosting sulforaphane. Flavonoids surged: quercetin (48 -191%), kaempferol (75 -172%), anthocyanins (42 -60%), with elevated PAL, CHS, 4CL, and C4H activities. Antioxidant and antimicrobial (40 -77%) potentials improved, though APCs and coliforms rose. Gamma radiation priming enhances growth, secondary metabolite accumulation, antioxidant potential, and antimicrobial activity in Brassicaceae sprouts. It offers a promising method to improve the nutritional and functional qualities of edible sprouts, aiding food safety and health.