Background/Objectives: Computed tomography (CT) of the head is vital in diagnosing neurological conditions but poses concerns regarding radiation exposure. Traditional diagnostic reference levels (DRLs) are based on anatomical regions, potentially overlooking variations in radiation requirements driven by clinical indication. This study aimed to establish clinical indication-specific DRLs (DRLCIs) for adult head CT to support precision benchmarking and optimise patient safety. Methods: A retrospective observational study was conducted using data from 378 adult patients undergoing non-contrast CT head scans between September 2022 and February 2024. Data on patient demographics, protocols, and radiation dose metrics (Computed Tomography Dose Index Volume and Dose–Length Product) were extracted using DoseWatch™ software. Protocol parameters were standardised across clinical indications such as trauma, stroke, headache, seizure, and infection. Descriptive statistics and correlation analyses were performed. Descriptive statistics, including means, standard deviations, and percentile distributions, were calculated. Correlation analyses were conducted using Pearson’s correlation coefficient to examine relationships between dose metrics and patient variables such as age and body mass index. Results: Mean CTDIvol values ranged from 50.58 mGy (trauma) to 52.90 mGy (infection), while DLP values ranged from 1052.52 to 1219.98 mGy·cm. Percentile distributions were narrow, indicating effective protocol standardisation. The strongest correlation was observed between CTDIvol and DLP (r = 0.89), while age and body mass index showed negligible influence on dose metrics. Comparative analysis showed alignment with international benchmarks from the UK, Qatar, Bahrain, and Nigeria. Conclusions: This study establishes DRLCIs for adult head CT, demonstrating consistent radiation dose delivery across indications with minimal variability. Clinical indication-based benchmarking enhances dose optimisation and aligns with global radiological protection frameworks.

Purpose: Different hospitals in Sudan detected a rare condition of liver vascular abnormalities characterized by vascular outflow impairment. The study aimed to describe the common radiological features of Budd Chiari syndrome and determine which feature is most frequently employed to characterize this condition during imaging techniques, primarily contrast-enhanced computed tomography (CECT) scans. Materials and methods: The study was conducted between March 2023 and June 2024 at Kuwaiti Specialized Hospital (KSH) and other diagnostic centers using a liver protocol on a CT machine (Optima 520 GE-CT machine, 16 slices). The study was a retrospective, cross-sectional, and review-based analysis of a rare study type carried out to characterize the state of BCS in Sudan; the CT scan’s findings on the liver, HVs, IVC, and abdomen were carefully assessed. The age range of the 61 patients who underwent a successful triphasic CT abdomen for the liver was 2–78 years. Results: The findings indicate that: the majority of patients 57.4% were male, the most common age groups were 39–52 years old, and the mean age at diagnosis was 45 years. BCS is primarily caused by hepatic veins (HVs) thrombosis, which is observed in 18.03% of cases, and HVs are not seen in 55.73% of cases. Liver parenchymal enhancement appears heterogenous in 27.87%, while heterogeneously enlarged liver was seen in 24.59%, and cirrhotic in 14.75% of BCS patients. In comparison, 59.01 percent of BCS patients arrived without varices. Varices were observed in the splenorenal and gastroesophageal regions in 37.7% of cases. Ascites accounted for the majority of BCS complications 73.77%, with SM vein blockage and squeezed duodenum accounting for 3.27% of each complication. The likelihood of developing ascites increases with age, and it is most common in patients between the ages of 39 and 52 years. Patient age had the greatest effect on the development of ascites. Conclusion: The common features of BCS as revealed by contrast-enhanced CT of the liver are non-visible HVs, venous occlusion at either level of HVs or IVC, caudate lobe enlargement, heterogenous, normal or enlarged liver, collateral venous varices at the splenorenal and gastroesophageal region, and ascites.

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis.

Introduction: Artificial intelligence (AI) for personalized computed tomography (CT) dose optimization has gained increasing attention due to its potential to reduce radiation exposure while maintaining image quality. CT dose optimization tailors according to individual patient characteristics, including age, weight, body composition, and clinical condition. The study aims to evaluate AI techniques in CT dose optimization, focusing on reducing radiation exposure while maintaining image quality. Methods: The relevant studies published between 2010 and 2024 were screened from databases, such as PubMed, IEEE Xplore, Scopus, ScienceDirect, and Web of Science, by following “Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)” guidelines and “Population, Intervention, Comparison, and Outcome ( PICO )” model. Risk of bias analysis revealed possible biases in studies. Meta-analysis was performed to elucidate the symmetry of the evidence and data from relevant studies. Results: The review underscores the significance of deep learning (DL) and machine learning (ML) algorithms, including reconstructive strategies, predictive modeling, and hybrid models, as critical in enhancing dose optimization. Conclusion: AI-driven approaches in CT dose optimization show promising potential to reduce radiation exposure safely and effectively without compromising image quality. Future research necessitating larger, diverse clinical trials are essential to validate these findings and ensure the equitable application of AI technologies across varied patient demographics. Studies have consistently indicated AI’s potential to reduce the dose exposure while maintaining the image and diagnostic quality. They highlight the importance of DL- and ML-based algorithms, such as reconstructive strategies, predictive modeling, and hybrid models, in CT dose optimization.