A closer look at the utilized radiation doses during computed tomography pulmonary angiography (CTPA) for COVID-19 patients.

Introduction: CTPA stands for computed tomography pulmonary angiography. CTPA is an X-ray imaging that combines X-rays and computer technology to create detailed images of the pulmonary arteries and veins in the lungs. This test diagnoses and monitors conditions like pulmonary embolism, arterial blockages, and hypertension. Coronavirus (COVID-19) has threatened world health over the last three years. The number of (CT) scans increased and played a vital role in diagnosing COVID-19 patients, including life-threatening pulmonary embolism (PE). This study aimed to assess the radiation dose resulted from CTPA for COVID-19 patients. Methods: Data were collected retrospectively from CTPA examinations on a single scanner in 84 symptomatic patients. The data collected included the dose length product (DLP), volumetric computed tomography dose index (CTDIvol), and size-specific dose estimate (SSDE). The organ dose and effective dose were estimated using VirtualDose software. Results: The study population included 84 patients, 52% male and 48% female, with an average age of 62. The average DLP, CTDIvol, and SSDE were 404.2 mGy cm, 13.5 mGy, and 11.6 mGy\, respectively. The mean effective doses (mSv) for males and females were 3.01 and 3.29, respectively. The maximum to minimum organ doses (mGy) between patients was 0.8 for the male bladder and 7.33 for the female lung. Conclusions: The increase in CT scans during the COVID-19 pandemic required close dose monitoring and optimization. The protocol used during CTPA should guarantee a minimum radiation dose with maximum patient benefits.

Investigation of the Radiographer's adherence and compliance with radiation protection and infection control practices during COVID-19 mobile radiography.

Radiological staff, especially radiographers, work as front liners against the COVID-19 outbreak. This study aims to assess compliance with radiation protection and infection control practices during COVID-19 mobile radiography procedures. This cross-sectional study included 234 radiographers (females, 56%, n = 131; males, 44%, n = 103) who were asked to complete an online questionnaire consisting of demographic data, radiation protection and infection control practices during COVID-19 portable cases, and knowledge and awareness. After informed consent was completed, SPSS statistical software was used for the data analysis. The most common age group of participants ranged from 18 to 25 years old (30.3%, n = 71). Bachelor's degree holders were 74.4% (n = 174). Most radiographers (39.7%, n = 93) had a working experience of 1-5 years, followed by 27.8% (n = 65) with more than 16 years of experience. Most respondents (62.4%, n = 146) handled approximately 1-5 cases daily, the majority of them (56%, n = 131) stated affirmatively they had obtained special training to handle COVID-19, and when inquired if they had received any special allowances for handling COVID-19 suspected/confirmed cases most of them stated negative (73.9%, n = 173). Most participants stated that they always wear a TLD during portable cases (67.1%, n = 157) and a lead apron (51.7%, n = 121). Around 73% (n = 171) knew the latest information on COVID-19 and attended the COVID-19 awareness course. A significant association was found between the work experience of the radiographers and their responses to following the best practices (p = 0.018, α = 0.05). Radiographers who had COVID-19 training (µ = 48.78) tend to adhere more to best practices than those who have not (p = 0.04, α = 0.05). Further, respondents who handled more than 16/more COVID-19 suspected/confirmed cases followed the best practices more (µ = 50.38) than those who handled less (p = 0.04, α = 0.05). This study revealed detailed information on radiation protection and infection control practices during COVID-19 mobile radiography. It has been observed that the participants/radiographers have good knowledge and awareness of radiation protection and infection-control practices. The present results may be used to plan future requirements regarding resources and training to ensure patient safety.

Radiation Dose Associated with Multi-Detector 64-Slice Computed Tomography Brain Examinations in Khartoum State, Sudan.

BACKGROUND: Radiation exposure due to computed tomography (CT) has become an important issue, as the number of CT examinations has been increasing worldwide. Radiation doses associated with CT are higher in comparison to other imaging procedures. CT-related radiation doses should be monitored and controlled in order to ensure reduction of radiation exposure and optimization of image quality. The aim of this study was to evaluate radiation doses in adult patient who underwent routine CT brain examinations, and to assess how CT scanning protocols affect patient doses in practice. MATERIAL/METHODS: A total of 118 patients underwent brain CT at two radiology departments equipped with 64-slice CT scanners, Khartoum, Sudan. Patient doses regarding weighted CT dose index (CTDIw) and dose length product (DLP) values were recorded. Quality control tests were performed for both scanners. RESULTS: The mean CTDIw values ranged from 62.9 to 65.8 mGy, DLP values ranged from 1003.7 to 1192.5 mGy, and the effective dose varied from 2.4 to 3.7 mSv. CONCLUSIONS: Patient doses in this study was higher compared to previous research, suggesting that patients exposed to unnecessary radiation. Therefore, optimization of radiation doses with the use of specified imaging protocols, well-documented indications for CT, training of technicians, and quality control programs will reduce the necessary radiation doses. Establishment of the diagnostic reference level is recommended for further dose reduction.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Reduction of metal artefacts from bilateral hip prostheses during lower extremity computed tomography angiography: an experimental phantom study.

INTRODUCTION: Image quality reduction due to metallic artefacts is a significant challenge during vascular computed tomography (CT) imaging of the lower extremities in patients with hip prostheses. This study aims to analyse various reconstruction algorithms' ability to reduce metal artefacts due to two types of hip prostheses during lower extremity CT angiography examinations. METHODS: A pelvis phantom was fabricated with the insertion of a tube filled with contrast media to simulate the femoral artery, and the phantom was then CT scanned with and without hip prostheses. Multimodal images were acquired using different kilovoltage peak (kVp) settings and reconstructed with different algorithms, such as filtered back projection (FBP), iterative reconstruction (iDose4), iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR). Image quality was assessed based on image noise, signal-to-noise ratio (SNR) and Hounsfield unit (HU) deviation. RESULTS: The IMR approach significantly improved image quality compared to iDose4 and FBP. For the vascular region, O-MAR improves SNR by 5 ± 1, 23 ± 5 and 42 ± 9 for FBP, iDose4 and IMR respectively, and improves HU precision towards the baseline values by 49% and 83% for FBP and IMR, respectively. The noise reduction was 71% and 89% for FBP and IMR, and 57% for iDose4. O-MAR greatly enhances SNR corrections among the most severe artefacts, with 29 ± 1 and 43 ± 4 for FBP and IMR, compared to iDose4 by 37 ± 7. CONCLUSION: IMR combined with O-MAR could improve the CT angiography of the lower extremities of patients with a hip prosthesis.

Evolving radiographic practice: Identifying possible skill requirements for future radiographers practicing in the United Arab Emirates (UAE).

AIM: This study aims to identify the prospective skill requirements for future radiographers practising in the United Arab Emirates (UAE). Such information will inform educational institutions, healthcare organisations, and policymakers in developing effective strategies. METHODS: A cross-sectional study was conducted involving currently practising radiographers, nuclear medicine technologists, sonographers, and radiation therapists in the UAE (n =74). A comprehensive survey questionnaire was developed and validated through piloting and expert consultations. Ethical approval was obtained, and data were collected through purposive sampling. Descriptive statistics, reliability analysis, Chi-square tests, and factor analysis were employed in the data analysis. RESULTS: The results showed that 73%, 47.3%, 43.2%, 40.5%, 39.2%, 33.8% interested in radiology safety, image interpretation, interprofessional and interpersonal skills, research and managerial skills, Picture Archiving and Communication System (PACS) administration and AI (Artificial Intelligence) and, clinical supervision and assessment, respectively. The factor analysis showed four factors factor considered for CPD training are training settings (15.12), training topics (1.88), CPD credits (1.72) and, presenter and expenses (1.49). CONCLUSION: This study sheds light on the CPD requirements and aspirations of radiographers in the UAE, offering insights into their preferences and challenges. These findings can inform strategies for improving CPD opportunities and ensuring that radiographers are equipped to meet the evolving healthcare demands in the UAE, including performing enhanced practice. IMPLICATIONS FOR PRACTICE: Development of flexible and comprehensive CPD programmes tailored to radiographers' career interests is required. Employers should provide financial support and flexibility in training options. Regulatory bodies should continue to mandate CPD, fostering a culture of lifelong learning. Supportive work environments, interdisciplinary collaboration, and technological fluency are crucial. Emphasising patient-centred care, research opportunities, and continuous assessment further enhances radiography practice.

Prevalence of smoking and its associated factors among students of the University of Dongola, Northern State, Sudan: a cross-sectional study.

Background: Smoking is one of the leading causes of morbidity and mortality worldwide, and its prevalence has increased globally, particularly among university students. Objective: The study aimed to assess the smoking prevalence and its associated factors among students at University of Dongola, Northern State, Sudan. Methods: A multi-centred cross-sectional study was conducted among students of University of Dongola. Data was collected via an online Google form questionnaire. Descriptive and comparative analyses were performed using SPSS, version 26. Statistical significance was considered at p less than or equal to 0.05. Results: A total of 642 students participated in the study, of which 51.9% were females. Most of the students (73.6%) were aged 20-25 years and came from health & medical faculties (60.7%). The overall prevalence of smoking was 11.7%. The determined risk factors for smoking included male gender (P≤0.001), older age (P≤0.001), non-health and non-medical faculties (P≤0.001), uneducated fathers (P=0.032), and low socio-economic status (P=0.001). The most common reason for smoking was stress (36%), with cigarettes being the most commonly used type (88%). Personal savings were the main source of smoking expenses (73.3%). Most smokers (88%) were aware of the harmful effects of smoking. Conclusion: The overall smoking prevalence was relatively low among students at University of Dongola. Male gender, older age, non-health and non-medical faculties, uneducated fathers, and low socio-economic status were significant risk factors for smoking. The majority of smokers were aware of the harmful effects of smoking.

A closer look at the current knowledge and prospects of artificial intelligence integration in dentistry practice: A cross-sectional study.

Background: Healthcare professionals have expressed worries about using AI, while others anticipate more work opportunities in the future and better patient care. Integrating AI into practice will directly impact dentistry practice. The purpose of the study is to evaluate organizational readiness, knowledge, attitude, and willingness to integrate AI into dentistry practice. Methods: a cross-sectional exploratory study of dentists, academic faculty and students who practice and study dentistry in UAE. Participants were invited to participate in a previously validated survey used to collect participants' demographics, knowledge, perceptions, and organizational readiness. Results: One hundred thirty-four responded to the survey with a response rate was 78% from the invited group. Results showed excitement to implement AI in practice accompanied by medium to high knowledge and a lack of education and training programs. As a result, organizations were not well prepared and had to ensure readiness for AI implementation. Conclusion: An effort to ensure professional and student readiness will improve AI integration in practice. In addition, dental professional societies and educational institutions must collaborate to develop proper training programs for dentists to close the knowledge gap.

Community and Policy Factors Influencing the Decision to Undergo Screening Mammography amongst Indian Women in the United Arab Emirates.

OBJECTIVE: This study explored the expectations, requirements, and recommendations and identifies the socio-ecological determinants for the informed uptake of screening mammography amongst expatriate women residing in the UAE. METHODOLOGY: A qualitative research approach was adopted using five focus group discussions. The study comprised two populations- those who had undergone screening mammography and those who had not. Thematic analysis- the six-phase model was adopted and modified for data analysis. RESULTS: The factors that influence the rate of uptake of screening mammography by the participants were multifactorial and reciprocal. The community determinants identified are cultural beliefs, medical professionals, breast cancer survivors, community organizations, and media. The health policies in an expatriate's home country and adopted country were found to have a considerable influence on the health behaviors of the participants. A comprehensive approach to change in health behavior is required to increase the informed uptake of screening mammography by expatriate women residing in the UAE. CONCLUSION: The study identified the need of developing migrant health units with community navigators, international health collaboration, migrant health directory, risk assessment team, automatic invitation system for screening, referral system, migrant cancer registry, and psychological support team in UAE. However, further studies are expected to give a wider perspective.

Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose.

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

Assessment of extremity dose for medical staff involved in positron emission tomography/computed tomography imaging: Retrospective study.

There has been an increase in positron emission tomography (PET)/computed tomography (CT) imaging procedures, and medical workers involved in PET/CT are at increased risk of occupational exposure. Data on extremity dose exposure are limited globally. The current study aimed to evaluate the occupational radiation dose for extremities for medical workers (nurses, radiographers/radiologic technologists, and nuclear medicine physicians) working in PET/CT scanners at 5 large hospitals in Turkey. Optically stimulated luminescence (OSL) and Thermoluminescent dosimeter (TLD) ring dosimeters were used to measure equivalent dose values. Hospitals 1, 2, and 5 used OSL, and 3 and 4 used TLD. A total of 502 readings were obtained from 55 workers. In millisievert (mSv), the average annual effective dose for all workers was 14.5 ±â€…17.7 (0.2-157.2). A radiography technologist received a maximum dose of 157.21. Nurses received the highest average annual effective dose (15.2 ±â€…19.46) (0.32-65.58), followed by radiography technologists (14.7 ±â€…18.03) (0.4-157.2), and nuclear medicine physicians demonstrated the least dose (8.6 ±â€…10.5) (1.2-24.4). The results show that the extremity dose is well below the annual dose limit of 500 mSv. However, there is a wide variation in dose among the workers, underlining a need for careful assessment of working conditions to ensure safe practices for all workers.

Exploring Radiographers' Engagement in Research: Motivation and Barriers in Five Arab Countries.

(1) Background: This study aims to comprehensively understand the motivations driving radiographers in five Arab countries to engage in research. (2) Methods: A cross-sectional study employing an anonymous online survey was conducted for 12 weeks from May to July 2023. The study sample consisted of 250 radiographers, with equal representation from Iraq, the Kingdom of Saudi Arabia, Palestine, Sudan, and the United Arab Emirates. (3) Results: Overall, the participants showed limited involvement in research-related activities in all five countries, particularly in presenting at conferences and publishing in peer-reviewed journals. Most participants believed research positively impacts their professional development (34.8%) and patient care and outcomes (40%). The participants perceived professional development (36.4%) as a key motivator for research engagement. A significant majority (81.6%) expressed motivation to start research in clinical practice. A total of 66.8% found research opportunities available during clinical practice. Barriers included time constraints (56%), limited resources (47.2%), and lack of support and skills (33.2% and 32%, respectively). (4) Conclusion: This study emphasises the need for targeted strategies to enhance research engagement among radiographers in the Arab region. Addressing barriers, such as time constraints and resource limitations, while leveraging intrinsic motivators, such as professional development, is crucial for fostering a culture of research-driven excellence in radiography.

Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice.

RATIONALE AND OBJECTIVES: This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice. MATERIALS AND METHODS: An electronically distributed cross-sectional study was conducted among radiologists and radiographers in the United Arab Emirates. The questionnaire captured the participants' demographics, qualifications, professional experience, and postgraduate training. Their knowledge, perception, organisational readiness, and challenges regarding AI integration into radiology were examined. RESULTS: There was a significant lack of knowledge and appreciation of the integration of AI into radiology practice. Organisations are stepping toward building AI implementation strategies. The availability of appropriate training courses is the main challenge for both radiographers and radiologists. CONCLUSION: The excitement of AI implementation into radiology practise was accompanied by a lack of knowledge and effort required to improve the user's appreciation of AI. The knowledge gap requires collaboration between educational institutes and professional bodies to develop structured training programs for radiologists and radiographers.

Infection control and radiation safety practices in the radiology department during the COVID-19 outbreak.

RATIONALE AND OBJECTIVES: Radiology personnel must have good knowledge, experience and adherence to radiation protection and infection control practices to ensure patient safety and prevent the further spread of the COVID-19 virus. This study analysed compliance and adherence to radiation protection and infection control during COVID-19 mobile radiography. METHODS: A cross-sectional using online survey was conducted from September to December 2021. Data on demographic characteristics, adherence to radiation protection and infection control practice were collected during mobile radiography for COVID-19 patients in the study. A random sample of the radiographers working in COVID-19 centres in the United Arab Emirates. RESULTS: Responses were received from 140 participants, with a response rate of 87.5%. Females were the predominant participants (n = 81; 58%). Participants aged ages between 18-25 years (n = 46; 33%) and 26-35 years (n = 42; 30%), (n = 57; 41%) had less than five years of experience, followed by participants who had more than 15 years (n = 38; 27%). Most participants (n = 81; 57.9%) stated that they performed approximately 1-5 suspected or confirmed COVID-19 cases daily. The participants had moderate to high adherence to radiation protection, with a mean and standard deviation of 42.3 ± 6.28. Additionally, infection control adherence was high, with 82% of the participants showing high adherence. CONCLUSION: Continuous guidance, training and follow-up are recommended to increase adherence and compliance to radiation protection and infection control compliance. Educational institutions and professional organisations must collaborate to provide structured training programmes for radiology practitioners to overcome the practice and knowledge gap.

Integration of artificial intelligence into nursing practice.

Background: Artificial Intelligence (AI) is developing at a rapid pace and finding new applications across the health service team. Some professionals have voiced concerns over the implementation of AI, whilst others predict greater job opportunities in the future. Nursing practice will be directly affected and further information is required on the knowledge and perceptions of nurses regarding the integration of AI in practice. The study aims to assess the knowledge, attitude, willingness, and organizational readiness in integrating AI into nursing practice. Methods: An exploratory cross-sectional survey of nurses working in health organisations. A survey link was emailed to participants. Nurses working in the United Arab Emirates (UAE) health organisations were invited to participate. Eligibility criteria included registered nurses in government or private hospitals. The survey captured the nurses demographic, knoweldage, preceptions, orgianizational readinesss and challenges regarding implementation of AI into nursing practice. Results: 553 responses were returned from 650 invitation giving a response rate of 85%. 51% of respondents stated their knowledge on AI was obtained through self-taught measures for most of the participants, while 20% of them gained it through various courses. Only 8% stated they learned through postgraduate courses, while 9% stated they lack knowledge of AI. 75% of all respondents agreed that the nursing curriculum should include some basic knowledge of AI. Conclusions: There is a lack of understanding of the principles of AI across the nursing profession. Further education and training is required to enable a seamless and safe integration of AI into nursing practice.

IMPACT OF RADIATION FIELD SIZE ON ABSORBED ORGAN DOSES IN NEONATES UNDERGOING CHEST RADIOGRAPHY IN AN ANTERIOR-POSTERIOR PROJECTION: A MONTE CARLO SIMULATION STUDY.

Electronic image cropping and poor collimation practices are used by some radiographers during paediatric radiography. Advantages of collimation should be investigated to disseminate convenient use among radiographers and create awareness. The aim of this study was to use Monte Carlo simulation to investigate the extent of the effect of collimation on the absorbed organ dose in neonates undergoing anterior-posterior chest examination. The minimum field size recommended by the European guidelines was calculated experimentally using a neonate phantom. A PCXMC version 2.0 simulation calculated the organ and effective doses at the minimum field size and at different field sizes. Increasing the field size by 1 cm in the head-to-feet direction increases the dose to the urinary bladder and prostate, whereas increasing the field size by 1 cm on all sides increases the dose to the upper limbs, ovaries, testicles and prostate. The use of an optimal field size reduces organ doses for neonates undergoing chest X-ray. Cropping X-ray images to reduce unnecessarily large field sizes results in unnecessary patient dosages and should be avoided. The primary beam should be restricted to expose only the area of interest, and image cropping should be discouraged.

Factors that affect student engagement in online learning in health professions education.

BACKGROUND: Student engagement is vital in achieving learning outcomes and improving students' motivation, involvement, and attitudes toward learning. The aim of this study was to identify the factors that affect student engagement in online learning in medical and health science colleges. METHODS: A cross-sectional study was conducted among medical and health science students and faculty in May 2020. The Online Engagement Strategies Questionnaire was e-mailed to 370 students and 102 faculty members. The SPSS statistical software was used for the statistical analysis. Exploratory factor analysis was performed to identify the factors that affected the students' engagement in online learning. Composite scores were calculated for factors, separately for the student and faculty responses. Descriptive statistical analysis was performed for the student and faculty responses. RESULTS: The response rates of the students and faculty were 85.1% and 74.1%, respectively. The mean ages of the students and faculty were 20.6 and 44.6 years, respectively. Eighty-five percent of the faculty (n = 51) and 88.3% (n = 278) of the students found that the use of technology in proper communication was an important strategy to engage students in online learning. The factor analysis revealed agreement between the students and faculty regarding the factors that support student engagement in online learning. Techno-pedagogical skills were considered very important for faculty and important for students. For both faculty and students, self-directed learning skills were important, and peer-assisted learning (PAL) was fair symmetry. Nevertheless, collaborative learning (CL) was fairly symmetry to students and important to faculty. CONCLUSION: To enhance student engagement in online learning, faculty members should consider improving their techno-pedagogical skills. Moreover, by incorporating self-directed learning, CL and PAL will support student engagement. Finally, faculty development, updating of course design, and institutional policies are all required to support online learning.

Development of acceptable quality radiation dose levels for common computed tomography examinations: A focused multicenter study in United Arab Emirates.

Purpose: Diagnostic Reference Level (DRL) is a practical tool for radiation dose optimization, yet it does not indicate the patient size or image quality. The Acceptable Quality Dose (AQD) introduced to address the limitations of the DRLs and it is based on image quality, radiation dose, and patient weight. The aim of this study is to establish the AQD for adult patients' undergoing Computed Tomography (CT) examinations (Head, chest, abdomen). Methods: This study is conducted in the four main hospitals at the Ministry of Health and Prevention. Patient information and exposure parameters were extracted. All the acceptable images are scored for their quality assessments. Data is classified as seven weight groups, <50, 50-59, 60-69, 70-79, 80-89, 90-99, and ≥100 kg. The mean ± SD, median, and 75th are calculated for the CTDIvol and DLP for each weight group per examination. Results: Out of 392, 358 CT examinations are scored with acceptable quality. The median CTDIvol values for the weight groups are obtained as 24.6, 25.4, 25.4, 25.0, 26.0, 27.0, and 29.0 mGy. Moreover, median DLP values are obtained as 576.7, 601.0, 616.5, 636.1, 654.0, 650.0, 780.0, and 622.5 mGy.cm, respectively, for head CT without Contrast Media (CM). Similar calculation for head CT with (CM), chest without CM, abdomen without CM, and chest and abdomen (with and without CM) CTs are presented. Conclusion: Images with bad, unacceptable and higher than necessary qualities contribute to increasing patient dose and increasing the DRLs. The AQD for the selected examinations were lower than the proposed DRLs in the United Arab Emirates. The integration of image quality and patients size in the assessment of the AQD values provide effective model to compare radiation dose indices within facility and compare with others. The obtained results may be useful in terms of improving dose and the diagnostic quality in the national and international levels.

Establishment of Diagnostic Reference Levels in Cone Beam Computed Tomography Scans in the United Arab Emirates.

This study aimed to address the knowledge gap in assessing the radiation doses from cone beam computed tomography (CBCT) procedures, establishing a typical value, and estimating effective and organ doses. A total of 340 patients aged 18-80 years were included in this study. Organ doses were estimated using VirtualDose IR software. The typical values were based on median values estimated as 1000 mGy cm2. The mean ED (µSv) per procedure was 149.5 ± 56, and the mean of the peak skin dose during the CBCT examination was 39.29 mGy. The highest organ dose was received by the salivary glands (2.71 mGy), the extrathoracic region (1.64 mGy), thyroid (1.24 mGy) and eyes (0.61 mGy). The patients' doses were higher than in previous studies. Staff awareness, education, training and dose optimisation are highly recommended. With the establishment of local DRLs, patient dosages can be reduced successfully without compromising image quality.

Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment.

Purpose: This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study. Methods: The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data. Results: The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance. Conclusion: It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.